linarphy/backscattering-analyzer
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

Update frequency cut arg and fit precision and range

Browse source
This commit is contained in:
linarphy 2024-05-31 17:50:29 +02:00
parent e391f463c4
commit 1e4dc0975b
No known key found for this signature in database
GPG key ID: E61920135EFF2295
2 changed files with 72 additions and 32 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

26
src/backscattering_analyzer/__init__.py
View file

@ -7,12 +7,16 @@ install(__name__)
logger = getLogger(__name__)
def show_projection(experiment: "Experiment") -> None:
def show_projection(
experiment: "Experiment",
start_frequency: float | None = None,
end_frequency: float | None = None,
) -> None:
"""
Show projection data with matplotlib
"""
logger.debug(_("showing experiment result"))
from matplotlib.pyplot import loglog, show, legend, close
from matplotlib.pyplot import loglog, show, legend, close, vlines
excited = experiment.signals["excited"].psd().sqrt()
reference = experiment.signals["reference"].psd().sqrt()
@ -28,6 +32,24 @@ def show_projection(experiment: "Experiment") -> None:
(experiment.projection + reference).y,
label="sum reference + excited",
) # type: ignore[ReportUnusedCallResult]
if start_frequency is not None:
vlines(
[
start_frequency,
],
min(experiment.projection.y),
max(experiment.projection.y),
color = 'k',
) # type: ignore[ReportUnusedCallResult]
if end_frequency is not None:
vlines(
[
end_frequency,
],
min(experiment.projection.y),
max(experiment.projection.y),
color = 'k',
) # type: ignore[ReportUnusedCallResult]
legend() # type: ignore[ReportUnusedCallResult]
show()
close()

34
src/backscattering_analyzer/experiment.py
View file

@ -1,7 +1,7 @@
from pathlib import Path
from tomllib import load
from numpy import argmin, float64, pi
from numpy import argmin, float64, pi, log10
from numpy.core.function_base import logspace
from numpy.core.multiarray import array
from numpy.typing import NDArray
@ -295,9 +295,11 @@ class Experiment:
def fit_factors(
self,
start: float = 15,
end: float = 100,
x: NDArray[float64] | None = None,
scatter_min: float = 1e-20,
scatter_max: float = 1,
start_frequency: float = 15,
end_frequency: float = 100,
precision: int = 3,
) -> dict[str, float]:
"""
Find the best factor (first order only) to get the projection
@ -311,10 +313,14 @@ class Experiment:
excited,
reference,
phase,
) = self.get_factors(start=start, end=end)
) = self.get_factors(start=start_frequency, end=end_frequency)
if x is None:
x = logspace(-10, 0, 1000)
for index in range(precision):
logger.debug(
_("search for a local minimum")
)
x = logspace(log10(scatter_min), log10(scatter_max), 1000)
y = array(
[
@ -339,7 +345,19 @@ class Experiment:
]
)
pre_scatter_factor: float = x[argmin(y)]
if argmin(y) == 0:
logger.warning(_("smaller than current range allows"))
scatter_max: float = x[1]
elif argmin(y) == len(x) - 1:
logger.warning(_("bigger than current range allows"))
scatter_min: float = x[-2]
else:
scatter_min: float = x[argmin(y) - 1]
scatter_max: float = x[argmin(y) + 1]
logger.debug(_("local minimum found"))
pre_scatter_factor: float = scatter_min / 2 + scatter_max / 2
logger.info(
_("found a scattering factor of {factor}").format(