import copy
import inspect
import functools
import warnings
import numpy as np
from . import poc
from .smooth import smooth_axis_monotone
[docs]class CannotSplitWarning(UserWarning):
pass
[docs]def preprocessing_step(identifier, name, steps_required=None,
steps_optional=None, options=None):
"""Decorator for Indentation preprocessors
The name and identifier are stored as a property of the wrapped
function.
Parameters
----------
identifier: str
identifier of the preprocessor (e.g. "correct_tip_offset")
name: str
human-readble name of the preprocessor
(e.g. "Estimate contact point")
steps_required: list of str
list of preprocessing steps that must be added before this
step
steps_optional: list of str
unlike `steps_required`, these steps do not have to be set,
but if they are set, they should come before this step
options: list of dict
if the preprocessor accepts optional keyword arguments,
this list yields valid values or dtypes
"""
def attribute_setter(func):
"""Decorator that sets the necessary attributes
The outer decorator is used to obtain the attributes.
This inner decorator returns the actual function that
wraps the preprocessor.
"""
func.identifier = identifier
assert isinstance(name, str)
func.name = name
func.options = options
func.steps_required = steps_required
func.steps_optional = steps_optional
return func
return attribute_setter
[docs]class IndentationPreprocessor(object):
[docs] @staticmethod
def apply(apret, identifiers=None, options=None, ret_details=False,
preproc_names=None):
"""Perform force-distance preprocessing steps
Parameters
----------
apret: nanite.Indentation
The afm data to preprocess
identifiers: list
A list of preprocessing identifiers that will be
applied (in the order given).
options: dict of dict
Preprocessing options for each identifier
ret_details:
Return preprocessing details dictionary
preproc_names: list
Deprecated - use `identifiers` instead
Notes
-----
This method is usually called from within the `Indentation`
class instance. If you are using this class directly and
apply it more than once, you might need to call
`apret.reset()` before preprocessing a second time.
"""
if preproc_names is not None:
identifiers = preproc_names
warnings.warn(
"Please use 'identifiers' instead of 'preproc_names'!",
DeprecationWarning)
details = {}
# Reset all user-defined data of the dataset, because we
# probably edited "tip position", "force", etc. If this
# is too slow in the future, then we would have to remember
# the previous preprocessing pipeline (such code is currently
# implemented on the other side in `indent.py` (2021-08-16).
apret.reset_data()
for ii, pid in enumerate(identifiers):
if pid in IndentationPreprocessor.available():
meth = IndentationPreprocessor.get_func(pid)
req = meth.steps_required
act = identifiers[:ii]
if req is not None and ((set(req) & set(act)) != set(req)):
raise ValueError(f"The preprocessing step '{pid}' requires"
f" the steps {meth.steps_required}!")
kwargs = options.get(pid, {}) # empty dict if not defined
if "ret_details" in inspect.signature(meth).parameters:
# only set `ret_details` if method accepts it
kwargs["ret_details"] = ret_details
details[pid] = meth(apret, **kwargs)
else:
msg = "The preprocessing method '{}' does not exist!"
raise KeyError(msg.format(pid))
# only return details if required
return details if ret_details else None
[docs] @staticmethod
def autosort(identifiers):
"""Automatically sort preprocessing identifiers
This takes into account `steps_required` and `steps_optional`.
"""
sorted_identifiers = copy.copy(identifiers)
for pid in identifiers:
meth = IndentationPreprocessor.get_func(pid)
steps_precursor = []
if meth.steps_required is not None:
steps_precursor += meth.steps_required
if meth.steps_optional is not None:
for ostep in meth.steps_optional:
if ostep in identifiers:
steps_precursor.append(ostep)
for step in steps_precursor:
# We have a requirement, check whether it is fulfilled
cix = sorted_identifiers.index(pid)
rix = sorted_identifiers.index(step)
if rix > cix:
# We pop the wrong requirement and insert it before
# the current pid.
sorted_identifiers.remove(step)
sorted_identifiers.insert(cix, step)
# Perform a sanity check
IndentationPreprocessor.check_order(sorted_identifiers)
return sorted_identifiers
[docs] @staticmethod
@functools.lru_cache()
def available():
"""Return list of available preprocessor identifiers"""
av = []
for key in dir(IndentationPreprocessor):
func = getattr(IndentationPreprocessor, key)
if hasattr(func, "identifier"):
av.append(func.identifier)
return IndentationPreprocessor.autosort(av)
[docs] @staticmethod
def check_order(identifiers):
"""Check preprocessing steps for correct order"""
for cix, pid in enumerate(identifiers):
meth = IndentationPreprocessor.get_func(pid)
if meth.steps_required:
rix = [identifiers.index(r) for r in meth.steps_required]
if np.any(np.array(rix) > cix):
raise ValueError(
f"Wrong required step order for {pid}: {identifiers}!")
if meth.steps_optional:
rio = []
for rr in meth.steps_optional:
if rr in identifiers:
rio.append(identifiers.index(rr))
if np.any(np.array(rio) > cix):
raise ValueError(
f"Wrong optional step order for {pid}: {identifiers}!")
[docs] @staticmethod
def get_func(identifier):
"""Return preprocessor function for identifier"""
for key in dir(IndentationPreprocessor):
func = getattr(IndentationPreprocessor, key)
if hasattr(func, "identifier") and func.identifier == identifier:
return func
else:
raise KeyError(f"Preprocessor '{identifier}' unknown!")
[docs] @staticmethod
def get_name(identifier):
"""Return preprocessor name for identifier"""
func = IndentationPreprocessor.get_func(identifier)
return func.name
[docs] @staticmethod
def get_steps_required(identifier):
"""Return requirement identifiers for identifier"""
func = IndentationPreprocessor.get_func(identifier)
return func.steps_required
[docs] @staticmethod
@preprocessing_step(identifier="compute_tip_position",
name="tip-sample separation")
def compute_tip_position(apret):
"""Perform tip-sample separation
Populate the "tip position" column by adding the force
normalized by the spring constant to the cantilever
height ("height (measured)").
This computation correctly reproduces the column
"Vertical Tip Position" as it is exported by the
JPK analysis software with the checked option
"Use Unsmoothed Height".
"""
has_hm = "height (measured)" in apret
has_fo = "force" in apret
has_sc = "spring constant" in apret.metadata
if "tip position" in apret.columns_innate:
# nothing to do
pass
elif has_hm and has_fo and has_sc:
k = apret.metadata["spring constant"]
force = apret["force"]
zcant = apret["height (measured)"]
apret["tip position"] = zcant + force / k
else:
missing = []
if not has_hm:
missing.append("missing data column 'height (measured)'")
if not has_fo:
missing.append("missing data column 'force'")
if not has_sc:
missing.append("missing metadata 'spring constant'")
mt = ", ".join(missing)
raise ValueError("Cannot compute tip position: {}".format(mt))
[docs] @staticmethod
@preprocessing_step(identifier="correct_force_offset",
name="baseline correction")
def correct_force_offset(apret):
"""Correct the force offset with an average baseline value
"""
idp = poc.compute_poc(force=apret["force"],
method="deviation_from_baseline")
if idp:
apret["force"] = apret["force"] - np.average(apret["force"][:idp])
else:
apret["force"] = apret["force"] - apret["force"][0]
[docs] @staticmethod
@preprocessing_step(
identifier="correct_tip_offset",
name="contact point estimation",
steps_required=["compute_tip_position"],
options=[
{"name": "method",
"type": str,
"choices": [p.identifier for p in poc.POC_METHODS],
"choices_human_readable": [p.name for p in poc.POC_METHODS]}
]
)
def correct_tip_offset(apret, method="deviation_from_baseline",
ret_details=False):
"""Estimate the point of contact
An estimate of the contact point is subtracted from the
tip position.
"""
data = poc.compute_poc(force=apret["force"],
method=method,
ret_details=ret_details)
if ret_details:
cpid, details = data
else:
cpid, details = data, None
apret["tip position"] = (apret["tip position"]
- apret["tip position"][cpid])
return details
[docs] @staticmethod
@preprocessing_step(identifier="correct_split_approach_retract",
name="segment discovery",
steps_required=["compute_tip_position"])
def correct_split_approach_retract(apret):
"""Split the approach and retract curves (farthest point method)
Approach and retract curves are defined by the microscope. When the
direction of piezo movement is flipped, the force at the sample tip
is still increasing. This can be either due to a time lag in the AFM
system or due to a residual force acting on the sample due to the
bent cantilever.
To repair this time lag, we append parts of the retract curve to the
approach curve, such that the curves are split at the minimum height.
"""
x = np.array(apret["tip position"], copy=True)
y = np.array(apret["force"], copy=True)
idp = poc.poc_deviation_from_baseline(y)
if idp and not np.isnan(idp):
# Flip and normalize tip position so that maximum is at minimum
# z-position (set to 1) which coincides with maximum indentation.
x -= x[idp]
x /= x.min()
x[x < 0] = 0
# Flip and normalize force so that maximum force is set to 1.
y -= np.average(y[:idp])
y /= y.max()
y[y < np.std(y[:idp])] = 0
idmin = np.argmax(x ** 2 + y ** 2)
segment = np.zeros(len(apret), dtype=np.uint8)
segment[idmin:] = 1
apret["segment"] = segment
else:
msg = "Cannot correct splitting of approach and retract curve " + \
"because the contact point position could not be estimated."
warnings.warn(msg, CannotSplitWarning)
[docs] @staticmethod
@preprocessing_step(identifier="smooth_height",
name="monotonic height data",
steps_optional=[
# Otherwise we lose the location of the point
# of deepest indentation:
"correct_split_approach_retract",
# Otherwise it might not be applied to
# "tip position":
"compute_tip_position"])
def smooth_height(apret):
"""Make height data monotonic
For the columns "height (measured)", "height (piezo), and
"tip position", this method ensures that the approach and
retract segments are monotonic.
"""
orig = ["height (measured)",
"height (piezo)",
"tip position"]
for col in orig:
if col not in apret:
continue
# Apply smoothing
sm_app = smooth_axis_monotone(apret.appr[col])
sm_ret = smooth_axis_monotone(apret.retr[col])
# Replace the column data
apret.appr[col] = sm_app
apret.retr[col] = sm_ret
#: Available preprocessors
available_preprocessors = IndentationPreprocessor.available()