import logging
import inspect
from typing import Dict, Type, TYPE_CHECKING
import numpy as np
from libertem import masks
from libertem.web.rpc import ProcedureProtocol
from .base import AnalysisResult, AnalysisResultSet
from .masks import BaseMasksAnalysis
from .helper import GeneratorHelper
# Keep imports for backwards compatibility!
from libertem.udf.com import ( # noqa: 401
com_masks_factory, com_masks_generic, center_shifts, apply_correction,
divergence, curl_2d, magnitude,
coordinate_check, GuessResult, guess_corrections
)
if TYPE_CHECKING:
from libertem.web.rpc import RPCContext
log = logging.getLogger(__name__)
class ComTemplate(GeneratorHelper):
short_name = "com"
api = "create_com_analysis"
temp = GeneratorHelper.temp_analysis
temp_analysis = temp + ["print(com_result)"]
channels = [
"field",
"magnitude",
"divergence",
"curl",
"x",
"y"
]
def __init__(self, params):
self.params = params
def get_dependency(self):
return [
"from empyre.vis.colors import ColormapCubehelix"
]
def get_docs(self):
title = "COM Analysis"
from libertem.api import Context
docs_rst = inspect.getdoc(Context.create_com_analysis)
docs = self.format_docs(title, docs_rst)
return docs
def convert_params(self):
params = ['dataset=ds']
for k in ['cx', 'cy']:
params.append(f'{k}={self.params[k]}')
params.append(f"mask_radius={self.params['r']}")
if self.params.get('flip_y', False):
params.append("flip_y=True")
if self.params.get('scan_rotation') is not None:
params.append(f"scan_rotation={self.params['scan_rotation']}")
if self.params.get('ri') is not None:
params.append(f"mask_radius_inner={self.params['ri']}")
return ', '.join(params)
def get_plot(self):
plot = [
"fig, axes = plt.subplots()",
'axes.set_title("field")',
"x_centers, y_centers = com_result.field.raw_data",
"ch = ColormapCubehelix(start=1, rot=1, minLight=0.5, maxLight=0.5, sat=2)",
"axes.imshow(ch.rgb_from_vector((x_centers, y_centers, 0)))"
]
for channel in self.channels[1:3]:
plot.append("fig, axes = plt.subplots()")
plot.append(f'axes.set_title("{channel}")')
plot.append(f'axes.imshow(com_result.{channel}.raw_data)')
return ['\n'.join(plot)]
def get_save(self):
save = []
for channel in self.channels:
save.append(f"np.save('com_result_{channel}.npy', com_result['{channel}'].raw_data)")
return '\n'.join(save)
[docs]class COMResultSet(AnalysisResultSet):
"""
Running a :class:`COMAnalysis` via :meth:`libertem.api.Context.run` on a dataset
returns an instance of this class.
This analysis is usually applied to datasets with real values. If the dataset contains
complex numbers, this result contains the keys :attr:`x_real`, :attr:`y_real`,
:attr:`x_imag`, :attr:`y_imag` instead of the vector field.
By default, the shift is given in pixel coordinates, i.e. positive x shift goes to the right
and positive y shift goes to the bottom. See also :ref:`concepts`.
.. versionchanged:: 0.6.0
The COM analysis now supports flipping the y axis and rotating the vectors.
.. versionadded:: 0.3.0
Attributes
----------
field : libertem.analysis.base.AnalysisResult
Center of mass shift relative to the center given to the analysis within the given radius
as a vector field with components (x, y). The visualized result uses a
cubehelix color wheel.
magnitude : libertem.analysis.base.AnalysisResult
Magnitude of the center of mass shift.
divergence : libertem.analysis.base.AnalysisResult
Divergence of the center of mass vector field at a given point
curl : libertem.analysis.base.AnalysisResult
Curl of the center of mass 2D vector field at a given point.
.. versionadded:: 0.6.0
x : libertem.analysis.base.AnalysisResult
X component of the center of mass shift
y : libertem.analysis.base.AnalysisResult
Y component of the center of mass shift
x_real : libertem.analysis.base.AnalysisResult
Real part of the x component of the center of mass shift (complex dataset only)
y_real : libertem.analysis.base.AnalysisResult
Real part of y component of the center of mass shift (complex dataset only)
x_imag : libertem.analysis.base.AnalysisResult
Imaginary part of the x component of the center of mass shift (complex dataset only)
y_imag : libertem.analysis.base.AnalysisResult
Imaginary part of y component of the center of mass shift (complex dataset only)
"""
pass
class ParameterGuessProc:
async def __call__(self, rpc_context: "RPCContext") -> Dict:
comp_ana = rpc_context.get_compound_analysis()
analyses = comp_ana["details"]["analyses"]
analysis_details = [
rpc_context.get_analysis_details(a)
for a in analyses
]
try:
com_analysis = [
a
for a in analysis_details
if a["details"]["analysisType"] == "CENTER_OF_MASS"
][0]
except IndexError:
return {
"status": "error",
"message": "no CoM analysis found",
}
com_analysis_id = com_analysis["analysis"]
if not rpc_context.have_analysis_results(com_analysis_id):
# run with the current analysis parameters as set in the GUI:
await rpc_context.run_analysis(com_analysis_id)
result_info = rpc_context.get_analysis_results(com_analysis_id)
res = result_info.results
old_params = result_info.details["parameters"]
guess = await rpc_context.run_sync(guess_corrections, res.y.raw_data, res.x.raw_data)
# NOTE: convert guess results to absolute values to make sure we don't
# run into any nasty synchronization issues, for example, if state goes
# stale after the guess button was clicked.
flip_y = bool(old_params["flip_y"]) != bool(guess.flip_y)
backtransformed = apply_correction(
y_centers=np.array((guess.cy, )),
x_centers=np.array((guess.cx, )),
scan_rotation=old_params["scan_rotation"],
flip_y=old_params["flip_y"],
forward=False,
)
return {
'status': 'ok',
'guess': {
'cx': backtransformed[1][0] + old_params["cx"],
'cy': backtransformed[0][0] + old_params["cy"],
'scan_rotation': guess.scan_rotation + old_params["scan_rotation"],
'flip_y': flip_y,
},
}
class COMAnalysis(BaseMasksAnalysis, id_="CENTER_OF_MASS"):
TYPE = 'UDF'
def get_udf_results(self, udf_results, roi, damage):
data = udf_results['intensity'].data
img_sum, img_y, img_x = (
data[..., 0],
data[..., 1],
data[..., 2],
)
return self.get_generic_results(img_sum, img_y, img_x, damage=damage)
def get_generic_results(self, img_sum, img_y, img_x, damage):
from libertem.viz import CMAP_CIRCULAR_DEFAULT, visualize_simple
ref_x = self.parameters["cx"]
ref_y = self.parameters["cy"]
y_centers_raw, x_centers_raw = center_shifts(img_sum, img_y, img_x, ref_y, ref_x)
shape = y_centers_raw.shape
y_centers, x_centers = apply_correction(
y_centers_raw, x_centers_raw,
scan_rotation=self.parameters["scan_rotation"],
flip_y=self.parameters["flip_y"]
)
if img_sum.dtype.kind == 'c':
x_real, x_imag = np.real(x_centers), np.imag(x_centers)
y_real, y_imag = np.real(y_centers), np.imag(y_centers)
return COMResultSet([
AnalysisResult(raw_data=x_real, visualized=visualize_simple(x_real, damage=damage),
key="x_real", title="x [real]", desc="x component of the center"),
AnalysisResult(raw_data=y_real, visualized=visualize_simple(y_real, damage=damage),
key="y_real", title="y [real]", desc="y component of the center"),
AnalysisResult(raw_data=x_imag, visualized=visualize_simple(x_imag, damage=damage),
key="x_imag", title="x [imag]", desc="x component of the center"),
AnalysisResult(raw_data=y_imag, visualized=visualize_simple(y_imag, damage=damage),
key="y_imag", title="y [imag]", desc="y component of the center"),
])
else:
damage = damage & np.isfinite(x_centers) & np.isfinite(y_centers)
# Make sure that an all-False `damage` is handled since np.max()
# trips on an empty array.
# As a remark -- the NumPy error message
# "zero-size array to reduction operation maximum which has no identity"
# is probably wrong since -np.inf is the identity element for maximum on
# floating point numbers and should be returned here.
if np.count_nonzero(damage) > 0:
vmax = np.sqrt(np.max(x_centers[damage]**2 + y_centers[damage]**2))
else:
vmax = 1
f = CMAP_CIRCULAR_DEFAULT.rgb_from_vector((x_centers, y_centers, 0), vmax=vmax)
m = magnitude(y_centers, x_centers)
# Create results which are valid for any nav_shape
results_list = [
AnalysisResult(
raw_data=(x_centers, y_centers),
visualized=f,
key="field", title="field", desc="cubehelix colorwheel visualization",
include_in_download=False
),
AnalysisResult(
raw_data=m,
visualized=visualize_simple(m, damage=damage),
key="magnitude", title="magnitude", desc="magnitude of the vector field"
),
AnalysisResult(
raw_data=x_centers,
visualized=visualize_simple(x_centers, damage=damage),
key="x", title="x", desc="x component of the center"
),
AnalysisResult(
raw_data=y_centers,
visualized=visualize_simple(y_centers, damage=damage),
key="y", title="y", desc="y component of the center"
),
]
# Add results which depend on np.gradient, i.e. all(nav_shape) > 1
if all([s > 1 for s in shape]):
d = divergence(y_centers, x_centers)
c = curl_2d(y_centers, x_centers)
extra_results = [
AnalysisResult(
raw_data=d,
visualized=visualize_simple(d, damage=damage),
key="divergence", title="divergence", desc="divergence of the vector field"
),
AnalysisResult(
raw_data=c,
visualized=visualize_simple(c, damage=damage),
key="curl", title="curl", desc="curl of the 2D vector field"
),
]
# Insert the results at position 2 for backwards compatibility/tests
# This could later be replaced with results_list.extend(extra_results)
results_list[2:2] = extra_results
return COMResultSet(results_list)
def get_mask_factories(self):
if self.dataset.shape.sig.dims != 2:
raise ValueError("can only handle 2D signals currently")
if self.parameters.get('ri'):
# annular CoM:
return com_masks_generic(
detector_y=self.dataset.shape.sig[0],
detector_x=self.dataset.shape.sig[1],
base_mask_factory=lambda: masks.ring(
imageSizeY=self.dataset.shape.sig[0],
imageSizeX=self.dataset.shape.sig[1],
centerY=self.parameters['cy'],
centerX=self.parameters['cx'],
radius=self.parameters['r'],
radius_inner=self.parameters['ri'],
)
)
else:
# CoM with radius cut-off:
return com_masks_factory(
detector_y=self.dataset.shape.sig[0],
detector_x=self.dataset.shape.sig[1],
cx=self.parameters['cx'],
cy=self.parameters['cy'],
r=self.parameters['r'],
)
def get_parameters(self, parameters: Dict) -> Dict:
(detector_y, detector_x) = self.dataset.shape.sig
cx = parameters.get('cx', detector_x / 2)
cy = parameters.get('cy', detector_y / 2)
r = parameters.get('r', float('inf'))
ri = parameters.get('ri', 0.0)
scan_rotation = parameters.get('scan_rotation', 0.)
flip_y = parameters.get('flip_y', False)
use_sparse = parameters.get('use_sparse', False)
return {
'cx': cx,
'cy': cy,
'r': r,
'ri': ri,
'scan_rotation': scan_rotation,
'flip_y': flip_y,
'use_sparse': use_sparse,
'mask_count': 3,
'mask_dtype': np.float32,
}
@classmethod
def get_template_helper(cls) -> Type[GeneratorHelper]:
return ComTemplate
@classmethod
def get_rpc_definitions(cls) -> Dict[str, Type[ProcedureProtocol]]:
return {
"guess_parameters": ParameterGuessProc,
}
def need_rerun(self, old_params: Dict, new_params: Dict) -> bool:
"""
Don't need to re-run UDF if only `flip_y` or `scan_rotation`
have changed.
"""
ignore_keys = {"flip_y", "scan_rotation"}
old_without_ignored = {
k: v
for k, v in old_params.items()
if k not in ignore_keys
}
new_without_ignored = {
k: v
for k, v in new_params.items()
if k not in ignore_keys
}
return old_without_ignored != new_without_ignored