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Initial structure.
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24
.gitignore
vendored
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24
.gitignore
vendored
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@ -0,0 +1,24 @@
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||||||
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# Byte-compiled / optimized / DLL files
|
||||||
|
__pycache__/
|
||||||
|
*.pyc
|
||||||
|
*$py.class
|
||||||
|
|
||||||
|
# Distribution / packaging
|
||||||
|
dist/
|
||||||
|
build/
|
||||||
|
*.egg-info/
|
||||||
|
|
||||||
|
# IDE-specific files
|
||||||
|
.idea/
|
||||||
|
.vscode/
|
||||||
|
|
||||||
|
# Logs
|
||||||
|
*.log
|
||||||
|
|
||||||
|
# Virtual environments
|
||||||
|
venv/
|
||||||
|
|
||||||
|
# Other
|
||||||
|
*.DS_Store
|
||||||
|
*.pos
|
||||||
|
*.quack
|
5
CHANGELOG.md
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5
CHANGELOG.md
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|
||||||
|
# Changelog
|
||||||
|
|
||||||
|
## Version 0.0.1 (15-04-2024)
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||||||
|
|
||||||
|
- Initial release
|
20
LICENSE
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20
LICENSE
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|
@ -0,0 +1,20 @@
|
||||||
|
MIT License
|
||||||
|
|
||||||
|
Copyright (c) 2023-2024 jupfi
|
||||||
|
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||||
|
of this software and associated documentation files (the "Software"), to deal
|
||||||
|
in the Software without restriction, including without limitation the rights
|
||||||
|
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||||
|
copies of the Software, and to permit persons to whom the Software is
|
||||||
|
furnished to do so, subject to the following conditions:
|
||||||
|
|
||||||
|
The above copyright notice and this permission notice shall be included in all
|
||||||
|
copies or substantial portions of the Software.
|
||||||
|
|
||||||
|
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||||
|
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||||
|
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||||
|
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||||
|
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||||
|
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||||
|
SOFTWARE.
|
1
README.md
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1
README.md
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|
@ -0,0 +1 @@
|
||||||
|
# quackseq
|
59
pyproject.toml
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59
pyproject.toml
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|
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|
||||||
|
[build-system]
|
||||||
|
requires = ["hatchling"]
|
||||||
|
build-backend = "hatchling.build"
|
||||||
|
|
||||||
|
[tool.hatch.metadata]
|
||||||
|
allow-direct-references = true
|
||||||
|
|
||||||
|
[project]
|
||||||
|
name = "quackseq"
|
||||||
|
version = "0.0.1"
|
||||||
|
authors = [
|
||||||
|
{ name="jupfi", email="support@nqrduck.cool" },
|
||||||
|
]
|
||||||
|
|
||||||
|
description = "Simple Python script to perform magnetic resonance spectroscopy experiments."
|
||||||
|
readme = "README.md"
|
||||||
|
license = { file="LICENSE" }
|
||||||
|
requires-python = ">=3.10"
|
||||||
|
|
||||||
|
classifiers = [
|
||||||
|
"Programming Language :: Python :: 3",
|
||||||
|
"License :: OSI Approved :: MIT License",
|
||||||
|
"Operating System :: OS Independent",
|
||||||
|
]
|
||||||
|
|
||||||
|
dependencies = [
|
||||||
|
"numpy",
|
||||||
|
"scipy",
|
||||||
|
]
|
||||||
|
|
||||||
|
[project.optional-dependencies]
|
||||||
|
dev = [
|
||||||
|
"black",
|
||||||
|
"pydocstyle",
|
||||||
|
"pyupgrade",
|
||||||
|
"ruff",
|
||||||
|
]
|
||||||
|
|
||||||
|
[tool.ruff]
|
||||||
|
|
||||||
|
[tool.ruff.lint]
|
||||||
|
extend-select = [
|
||||||
|
"UP", # pyupgrade
|
||||||
|
"D", # pydocstyle
|
||||||
|
]
|
||||||
|
|
||||||
|
[tool.ruff.lint.per-file-ignores]
|
||||||
|
"__init__.py" = ["F401"]
|
||||||
|
|
||||||
|
[tool.ruff.lint.pydocstyle]
|
||||||
|
convention = "google"
|
||||||
|
|
||||||
|
[project.urls]
|
||||||
|
"Homepage" = "https://nqrduck.cool"
|
||||||
|
"Bug Tracker" = "https://github.com/nqrduck/quackseq/issues"
|
||||||
|
"Source Code" = "https://github.com/nqrduck/quackseq"
|
||||||
|
|
||||||
|
[tool.hatch.build.targets.wheel]
|
||||||
|
packages = ["src/quackseq"]
|
0
src/quackseq/__init__.py
Normal file
0
src/quackseq/__init__.py
Normal file
95
src/quackseq/event.py
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95
src/quackseq/event.py
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|
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|
||||||
|
import logging
|
||||||
|
from collections import OrderedDict
|
||||||
|
|
||||||
|
from quackseq.pulseparameters import Option
|
||||||
|
from quackseq.helpers import UnitConverter
|
||||||
|
|
||||||
|
logger = logging.getLogger(__name__)
|
||||||
|
|
||||||
|
|
||||||
|
class Event:
|
||||||
|
"""An event is a part of a pulse sequence. It has a name and a duration and different parameters that have to be set.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
name (str): The name of the event
|
||||||
|
duration (str): The duration of the event
|
||||||
|
|
||||||
|
Attributes:
|
||||||
|
name (str): The name of the event
|
||||||
|
duration (str): The duration of the event
|
||||||
|
parameters (OrderedDict): The parameters of the event
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, name: str, duration: str) -> None:
|
||||||
|
"""Initializes the event."""
|
||||||
|
self.parameters = OrderedDict()
|
||||||
|
self.name = name
|
||||||
|
self.duration = duration
|
||||||
|
|
||||||
|
def add_parameter(self, parameter) -> None:
|
||||||
|
"""Adds a parameter to the event.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
parameter: The parameter to add
|
||||||
|
"""
|
||||||
|
self.parameters.append(parameter)
|
||||||
|
|
||||||
|
def on_duration_changed(self, duration: str) -> None:
|
||||||
|
"""This method is called when the duration of the event is changed.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
duration (str): The new duration of the event
|
||||||
|
"""
|
||||||
|
logger.debug("Duration of event %s changed to %s", self.name, duration)
|
||||||
|
self.duration = duration
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
def load_event(cls, event, pulse_parameter_options):
|
||||||
|
"""Loads an event from a dict.
|
||||||
|
|
||||||
|
The pulse paramter options are needed to load the parameters
|
||||||
|
and determine if the correct spectrometer is active.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
event (dict): The dict with the event data
|
||||||
|
pulse_parameter_options (dict): The dict with the pulse parameter options
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Event: The loaded event
|
||||||
|
"""
|
||||||
|
obj = cls(event["name"], event["duration"])
|
||||||
|
for parameter in event["parameters"]:
|
||||||
|
for pulse_parameter_option in pulse_parameter_options.keys():
|
||||||
|
# This checks if the pulse paramter options are the same as the ones in the pulse sequence
|
||||||
|
if pulse_parameter_option == parameter["name"]:
|
||||||
|
pulse_parameter_class = pulse_parameter_options[
|
||||||
|
pulse_parameter_option
|
||||||
|
]
|
||||||
|
obj.parameters[pulse_parameter_option] = pulse_parameter_class(
|
||||||
|
parameter["name"]
|
||||||
|
)
|
||||||
|
# Delete the default instances of the pulse parameter options
|
||||||
|
obj.parameters[pulse_parameter_option].options = []
|
||||||
|
for option in parameter["value"]:
|
||||||
|
obj.parameters[pulse_parameter_option].options.append(
|
||||||
|
Option.from_json(option)
|
||||||
|
)
|
||||||
|
|
||||||
|
return obj
|
||||||
|
|
||||||
|
@property
|
||||||
|
def duration(self):
|
||||||
|
"""The duration of the event."""
|
||||||
|
return self._duration
|
||||||
|
|
||||||
|
@duration.setter
|
||||||
|
def duration(self, duration: str):
|
||||||
|
# Duration needs to be a positive number
|
||||||
|
try:
|
||||||
|
duration = UnitConverter.to_float(duration)
|
||||||
|
except ValueError:
|
||||||
|
raise ValueError("Duration needs to be a number")
|
||||||
|
if duration < 0:
|
||||||
|
raise ValueError("Duration needs to be a positive number")
|
||||||
|
|
||||||
|
self._duration = duration
|
349
src/quackseq/functions.py
Normal file
349
src/quackseq/functions.py
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|
@ -0,0 +1,349 @@
|
||||||
|
"""A module that contains functions that can be used in various modules. This used to be part of the nqrduck-spectrometer module."""
|
||||||
|
|
||||||
|
from __future__ import annotations
|
||||||
|
import logging
|
||||||
|
import numpy as np
|
||||||
|
import sympy
|
||||||
|
|
||||||
|
logger = logging.getLogger(__name__)
|
||||||
|
|
||||||
|
|
||||||
|
class Function:
|
||||||
|
"""A function that can be used as a pulse parameter.
|
||||||
|
|
||||||
|
This class is the base class for all functions that can be used as pulse parameters. Functions can be used for pulse shapes, for example.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
expr (str | sympy.Expr): The expression of the function.
|
||||||
|
|
||||||
|
Attributes:
|
||||||
|
name (str): The name of the function.
|
||||||
|
parameters (list): The parameters of the function.
|
||||||
|
expr (sympy.Expr): The sympy expression of the function.
|
||||||
|
resolution (float): The resolution of the function in seconds.
|
||||||
|
start_x (float): The x value where the evalution of the function starts.
|
||||||
|
end_x (float): The x value where the evalution of the function ends.
|
||||||
|
"""
|
||||||
|
|
||||||
|
name: str
|
||||||
|
parameters: list
|
||||||
|
expression: str | sympy.Expr
|
||||||
|
resolution: float
|
||||||
|
start_x: float
|
||||||
|
end_x: float
|
||||||
|
|
||||||
|
subclasses = []
|
||||||
|
|
||||||
|
def __init_subclass__(cls, **kwargs):
|
||||||
|
"""Registers the subclass."""
|
||||||
|
super().__init_subclass__(**kwargs)
|
||||||
|
cls.subclasses.append(cls)
|
||||||
|
|
||||||
|
def __init__(self, expr) -> None:
|
||||||
|
"""Initializes the function."""
|
||||||
|
self.parameters = []
|
||||||
|
self.expr = expr
|
||||||
|
self.resolution = 1 / 30.72e6
|
||||||
|
self.start_x = -1
|
||||||
|
self.end_x = 1
|
||||||
|
|
||||||
|
def get_time_points(self, pulse_length: float) -> np.ndarray:
|
||||||
|
"""Returns the time domain points for the function with the given pulse length.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
pulse_length (float): The pulse length in seconds.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
np.ndarray: The time domain points.
|
||||||
|
"""
|
||||||
|
# Get the time domain points
|
||||||
|
n = int(pulse_length / self.resolution)
|
||||||
|
t = np.linspace(0, float(pulse_length), n)
|
||||||
|
return t
|
||||||
|
|
||||||
|
def evaluate(self, pulse_length: float, resolution: float = None) -> np.ndarray:
|
||||||
|
"""Evaluates the function for the given pulse length.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
pulse_length (float): The pulse length in seconds.
|
||||||
|
resolution (float, optional): The resolution of the function in seconds. Defaults to None.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
np.ndarray: The evaluated function.
|
||||||
|
"""
|
||||||
|
if resolution is None:
|
||||||
|
resolution = self.resolution
|
||||||
|
n = int(pulse_length / resolution)
|
||||||
|
t = np.linspace(self.start_x, self.end_x, n)
|
||||||
|
x = sympy.symbols("x")
|
||||||
|
|
||||||
|
found_variables = dict()
|
||||||
|
# Create a dictionary of the parameters and their values
|
||||||
|
for parameter in self.parameters:
|
||||||
|
found_variables[parameter.symbol] = parameter.value
|
||||||
|
|
||||||
|
final_expr = self.expr.subs(found_variables)
|
||||||
|
# If the expression is a number (does not depend on x), return an array of that number
|
||||||
|
if final_expr.is_number:
|
||||||
|
return np.full(t.shape, float(final_expr))
|
||||||
|
|
||||||
|
f = sympy.lambdify([x], final_expr, "numpy")
|
||||||
|
|
||||||
|
return f(t)
|
||||||
|
|
||||||
|
def get_tdx(self, pulse_length: float) -> np.ndarray:
|
||||||
|
"""Returns the time domain points and the evaluated function for the given pulse length.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
pulse_length (float): The pulse length in seconds.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
np.ndarray: The time domain points.
|
||||||
|
"""
|
||||||
|
n = int(pulse_length / self.resolution)
|
||||||
|
t = np.linspace(self.start_x, self.end_x, n)
|
||||||
|
return t
|
||||||
|
|
||||||
|
def get_pulse_amplitude(
|
||||||
|
self, pulse_length: float, resolution: float = None
|
||||||
|
) -> np.array:
|
||||||
|
"""Returns the pulse amplitude in the time domain.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
pulse_length (Float): The pulse length in seconds.
|
||||||
|
resolution (float, optional): The resolution of the function in seconds. Defaults to None.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
np.array: The pulse amplitude.
|
||||||
|
"""
|
||||||
|
return self.evaluate(pulse_length, resolution=resolution)
|
||||||
|
|
||||||
|
def add_parameter(self, parameter: Function.Parameter) -> None:
|
||||||
|
"""Adds a parameter to the function.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
parameter (Function.Parameter): The parameter to add.
|
||||||
|
"""
|
||||||
|
self.parameters.append(parameter)
|
||||||
|
|
||||||
|
def to_json(self) -> dict:
|
||||||
|
"""Returns a json representation of the function.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
dict: The json representation of the function.
|
||||||
|
"""
|
||||||
|
return {
|
||||||
|
"name": self.name,
|
||||||
|
"class" : self.__class__.__name__,
|
||||||
|
"parameters": [parameter.to_json() for parameter in self.parameters],
|
||||||
|
"expression": str(self.expr),
|
||||||
|
"resolution": self.resolution,
|
||||||
|
"start_x": self.start_x,
|
||||||
|
"end_x": self.end_x,
|
||||||
|
}
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
def from_json(cls, data: dict) -> Function:
|
||||||
|
"""Creates a function from a json representation.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
data (dict): The json representation of the function.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Function: The function.
|
||||||
|
"""
|
||||||
|
logger.debug(f"Data: {data}")
|
||||||
|
for subclass in cls.subclasses:
|
||||||
|
logger.debug("Checking subclass %s", subclass)
|
||||||
|
logger.debug("Subclass name %s", subclass.__name__)
|
||||||
|
if subclass.__name__ == data["class"]:
|
||||||
|
cls = subclass
|
||||||
|
logger.debug("Found subclass %s", cls)
|
||||||
|
break
|
||||||
|
|
||||||
|
obj = cls()
|
||||||
|
obj.expr = data["expression"]
|
||||||
|
obj.name = data["name"]
|
||||||
|
obj.resolution = data["resolution"]
|
||||||
|
obj.start_x = data["start_x"]
|
||||||
|
obj.end_x = data["end_x"]
|
||||||
|
|
||||||
|
obj.parameters = []
|
||||||
|
for parameter in data["parameters"]:
|
||||||
|
obj.add_parameter(Function.Parameter.from_json(parameter))
|
||||||
|
|
||||||
|
return obj
|
||||||
|
|
||||||
|
@property
|
||||||
|
def expr(self):
|
||||||
|
"""The sympy expression of the function."""
|
||||||
|
return self._expr
|
||||||
|
|
||||||
|
@expr.setter
|
||||||
|
def expr(self, expr):
|
||||||
|
if isinstance(expr, str):
|
||||||
|
try:
|
||||||
|
self._expr = sympy.sympify(expr)
|
||||||
|
except ValueError:
|
||||||
|
logger.error(f"Could not convert {expr} to a sympy expression")
|
||||||
|
raise SyntaxError(f"Could not convert {expr} to a sympy expression")
|
||||||
|
elif isinstance(expr, sympy.Expr):
|
||||||
|
self._expr = expr
|
||||||
|
|
||||||
|
@property
|
||||||
|
def resolution(self):
|
||||||
|
"""The resolution of the function in seconds."""
|
||||||
|
return self._resolution
|
||||||
|
|
||||||
|
@resolution.setter
|
||||||
|
def resolution(self, resolution):
|
||||||
|
try:
|
||||||
|
self._resolution = float(resolution)
|
||||||
|
except ValueError:
|
||||||
|
logger.error("Could not convert %s to a float", resolution)
|
||||||
|
raise SyntaxError(f"Could not convert {resolution} to a float")
|
||||||
|
|
||||||
|
@property
|
||||||
|
def start_x(self):
|
||||||
|
"""The x value where the evalution of the function starts."""
|
||||||
|
return self._start_x
|
||||||
|
|
||||||
|
@start_x.setter
|
||||||
|
def start_x(self, start_x):
|
||||||
|
try:
|
||||||
|
self._start_x = float(start_x)
|
||||||
|
except ValueError:
|
||||||
|
logger.error("Could not convert %s to a float", start_x)
|
||||||
|
raise SyntaxError(f"Could not convert {start_x} to a float")
|
||||||
|
|
||||||
|
@property
|
||||||
|
def end_x(self):
|
||||||
|
"""The x value where the evalution of the function ends."""
|
||||||
|
return self._end_x
|
||||||
|
|
||||||
|
@end_x.setter
|
||||||
|
def end_x(self, end_x):
|
||||||
|
try:
|
||||||
|
self._end_x = float(end_x)
|
||||||
|
except ValueError:
|
||||||
|
logger.error("Could not convert %s to a float", end_x)
|
||||||
|
raise SyntaxError(f"Could not convert {end_x} to a float")
|
||||||
|
|
||||||
|
class Parameter:
|
||||||
|
"""A parameter of a function.
|
||||||
|
|
||||||
|
This can be for example the standard deviation of a Gaussian function.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
name (str): The name of the parameter.
|
||||||
|
symbol (str): The symbol of the parameter.
|
||||||
|
value (float): The value of the parameter.
|
||||||
|
|
||||||
|
Attributes:
|
||||||
|
name (str): The name of the parameter.
|
||||||
|
symbol (str): The symbol of the parameter.
|
||||||
|
value (float): The value of the parameter.
|
||||||
|
default (float): The default value of the parameter.
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, name: str, symbol: str, value: float) -> None:
|
||||||
|
"""Initializes the parameter."""
|
||||||
|
self.name = name
|
||||||
|
self.symbol = symbol
|
||||||
|
self.value = value
|
||||||
|
self.default = value
|
||||||
|
|
||||||
|
def set_value(self, value: float) -> None:
|
||||||
|
"""Sets the value of the parameter.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
value (float): The new value of the parameter.
|
||||||
|
"""
|
||||||
|
self.value = value
|
||||||
|
logger.debug("Parameter %s set to %s", self.name, self.value)
|
||||||
|
|
||||||
|
def to_json(self) -> dict:
|
||||||
|
"""Returns a json representation of the parameter.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
dict: The json representation of the parameter.
|
||||||
|
"""
|
||||||
|
return {
|
||||||
|
"name": self.name,
|
||||||
|
"symbol": self.symbol,
|
||||||
|
"value": self.value,
|
||||||
|
"default": self.default,
|
||||||
|
}
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
def from_json(cls, data):
|
||||||
|
"""Creates a parameter from a json representation.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
data (dict): The json representation of the parameter.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Function.Parameter: The parameter.
|
||||||
|
"""
|
||||||
|
obj = cls(data["name"], data["symbol"], data["value"])
|
||||||
|
obj.default = data["default"]
|
||||||
|
return obj
|
||||||
|
|
||||||
|
|
||||||
|
class RectFunction(Function):
|
||||||
|
"""The rectangular function."""
|
||||||
|
|
||||||
|
name = "Rectangular"
|
||||||
|
|
||||||
|
def __init__(self) -> None:
|
||||||
|
"""Initializes the RecFunction."""
|
||||||
|
expr = sympy.sympify("1")
|
||||||
|
super().__init__(expr)
|
||||||
|
|
||||||
|
|
||||||
|
class SincFunction(Function):
|
||||||
|
"""The sinc function.
|
||||||
|
|
||||||
|
The sinc function is defined as sin(x * l) / (x * l).
|
||||||
|
The parameter is the scale factor l.
|
||||||
|
"""
|
||||||
|
|
||||||
|
name = "Sinc"
|
||||||
|
|
||||||
|
def __init__(self) -> None:
|
||||||
|
"""Initializes the SincFunction."""
|
||||||
|
expr = sympy.sympify("sin(x * l)/ (x * l)")
|
||||||
|
super().__init__(expr)
|
||||||
|
self.add_parameter(Function.Parameter("Scale Factor", "l", 2))
|
||||||
|
self.start_x = -np.pi
|
||||||
|
self.end_x = np.pi
|
||||||
|
|
||||||
|
|
||||||
|
class GaussianFunction(Function):
|
||||||
|
"""The Gaussian function.
|
||||||
|
|
||||||
|
The Gaussian function is defined as exp(-0.5 * ((x - mu) / sigma)**2).
|
||||||
|
The parameters are the mean and the standard deviation.
|
||||||
|
"""
|
||||||
|
|
||||||
|
name = "Gaussian"
|
||||||
|
|
||||||
|
def __init__(self) -> None:
|
||||||
|
"""Initializes the GaussianFunction."""
|
||||||
|
expr = sympy.sympify("exp(-0.5 * ((x - mu) / sigma)**2)")
|
||||||
|
super().__init__(expr)
|
||||||
|
self.add_parameter(Function.Parameter("Mean", "mu", 0))
|
||||||
|
self.add_parameter(Function.Parameter("Standard Deviation", "sigma", 1))
|
||||||
|
self.start_x = -np.pi
|
||||||
|
self.end_x = np.pi
|
||||||
|
|
||||||
|
|
||||||
|
class CustomFunction(Function):
|
||||||
|
"""A custom function."""
|
||||||
|
|
||||||
|
name = "Custom"
|
||||||
|
|
||||||
|
def __init__(self) -> None:
|
||||||
|
"""Initializes the Custom Function."""
|
||||||
|
expr = sympy.sympify(" 2 * x**2 + 3 * x + 1")
|
||||||
|
super().__init__(expr)
|
51
src/quackseq/helpers.py
Normal file
51
src/quackseq/helpers.py
Normal file
|
@ -0,0 +1,51 @@
|
||||||
|
"""Helper used for unit conversion."""
|
||||||
|
|
||||||
|
import decimal
|
||||||
|
|
||||||
|
|
||||||
|
class UnitConverter:
|
||||||
|
"""This class provides methods for unit conversion."""
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
def to_decimal(cls, value: str) -> decimal.Decimal:
|
||||||
|
"""This method checks if the last character of the string is a suffix.
|
||||||
|
|
||||||
|
The available suffixes are:
|
||||||
|
- n for nano
|
||||||
|
- u for micro
|
||||||
|
- m for milli
|
||||||
|
|
||||||
|
Args:
|
||||||
|
value (str): The value to be converted
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
decimal.Decimal: The converted value
|
||||||
|
"""
|
||||||
|
try:
|
||||||
|
if value[-1] == "n":
|
||||||
|
return decimal.Decimal(value[:-1]) * decimal.Decimal("1e-9")
|
||||||
|
elif value[-1] == "u":
|
||||||
|
return decimal.Decimal(value[:-1]) * decimal.Decimal("1e-6")
|
||||||
|
elif value[-1] == "m":
|
||||||
|
return decimal.Decimal(value[:-1]) * decimal.Decimal("1e-3")
|
||||||
|
else:
|
||||||
|
return decimal.Decimal(value)
|
||||||
|
except TypeError:
|
||||||
|
return decimal.Decimal(value)
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
def to_float(cls, value: str) -> float:
|
||||||
|
"""This method checks if the last character of the string is a suffix.
|
||||||
|
|
||||||
|
The available suffixes are:
|
||||||
|
- n for nano
|
||||||
|
- u for micro
|
||||||
|
- m for milli
|
||||||
|
|
||||||
|
Args:
|
||||||
|
value (str): The value to be converted
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
float: The converted value
|
||||||
|
"""
|
||||||
|
return float(cls.to_decimal(value))
|
364
src/quackseq/measurement.py
Normal file
364
src/quackseq/measurement.py
Normal file
|
@ -0,0 +1,364 @@
|
||||||
|
"""This module defines the measurement data structure and the fit class for measurement data."""
|
||||||
|
|
||||||
|
import logging
|
||||||
|
import numpy as np
|
||||||
|
from scipy.optimize import curve_fit
|
||||||
|
|
||||||
|
from quackseq.functions import Function
|
||||||
|
from quackseq.signalprocessing import SignalProcessing as sp
|
||||||
|
|
||||||
|
logger = logging.getLogger(__name__)
|
||||||
|
|
||||||
|
|
||||||
|
class Measurement:
|
||||||
|
"""This class defines how measurement data should look.
|
||||||
|
|
||||||
|
It includes pulse parameters necessary for further signal processing.
|
||||||
|
Every spectrometer should adhere to this data structure in order to be compatible with the rest of the nqrduck.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
name (str): Name of the measurement.
|
||||||
|
tdx (np.array): Time axis for the x axis of the measurement data.
|
||||||
|
tdy (np.array): Time axis for the y axis of the measurement data.
|
||||||
|
target_frequency (float): Target frequency of the measurement.
|
||||||
|
frequency_shift (float, optional): Frequency shift of the measurement. Defaults to 0.
|
||||||
|
IF_frequency (float, optional): Intermediate frequency of the measurement. Defaults to 0.
|
||||||
|
|
||||||
|
Attributes:
|
||||||
|
tdx (np.array): Time axis for the x axis of the measurement data.
|
||||||
|
tdy (np.array): Time axis for the y axis of the measurement data.
|
||||||
|
target_frequency (float): Target frequency of the measurement.
|
||||||
|
frequency_shift (float): Frequency shift of the measurement.
|
||||||
|
IF_frequency (float): Intermediate frequency of the measurement.
|
||||||
|
fdx (np.array): Frequency axis for the x axis of the measurement data.
|
||||||
|
fdy (np.array): Frequency axis for the y axis of the measurement data.
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(
|
||||||
|
self,
|
||||||
|
name: str,
|
||||||
|
tdx: np.array,
|
||||||
|
tdy: np.array,
|
||||||
|
target_frequency: float,
|
||||||
|
frequency_shift: float = 0,
|
||||||
|
IF_frequency: float = 0,
|
||||||
|
) -> None:
|
||||||
|
"""Initializes the measurement."""
|
||||||
|
self.name = name
|
||||||
|
self.tdx = tdx
|
||||||
|
self.tdy = tdy
|
||||||
|
self.target_frequency = target_frequency
|
||||||
|
self.frequency_shift = frequency_shift
|
||||||
|
self.IF_frequency = IF_frequency
|
||||||
|
self.fdx, self.fdy = sp.fft(tdx, tdy, frequency_shift)
|
||||||
|
self.fits = []
|
||||||
|
|
||||||
|
def apodization(self, function: Function) -> "Measurement":
|
||||||
|
"""Applies apodization to the measurement data.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
function (Function): Apodization function.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Measurement: The apodized measurement.
|
||||||
|
"""
|
||||||
|
duration = (self.tdx[-1] - self.tdx[0]) * 1e-6
|
||||||
|
resolution = duration / len(self.tdx)
|
||||||
|
logger.debug("Resolution: %s", resolution)
|
||||||
|
|
||||||
|
y_weight = function.get_pulse_amplitude(duration, resolution)
|
||||||
|
tdy_apodized = self.tdy * y_weight
|
||||||
|
|
||||||
|
apodized_measurement = Measurement(
|
||||||
|
self.name,
|
||||||
|
self.tdx,
|
||||||
|
tdy_apodized,
|
||||||
|
target_frequency=self.target_frequency,
|
||||||
|
IF_frequency=self.IF_frequency,
|
||||||
|
)
|
||||||
|
return apodized_measurement
|
||||||
|
|
||||||
|
def add_fit(self, fit: "Fit") -> None:
|
||||||
|
"""Adds a fit to the measurement.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
fit (Fit): The fit to add.
|
||||||
|
"""
|
||||||
|
self.fits.append(fit)
|
||||||
|
|
||||||
|
def delete_fit(self, fit: "Fit") -> None:
|
||||||
|
"""Deletes a fit from the measurement.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
fit (Fit): The fit to delete.
|
||||||
|
"""
|
||||||
|
self.fits.remove(fit)
|
||||||
|
|
||||||
|
def edit_fit_name(self, fit: "Fit", name: str) -> None:
|
||||||
|
"""Edits the name of a fit.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
fit (Fit): The fit to edit.
|
||||||
|
name (str): The new name.
|
||||||
|
"""
|
||||||
|
logger.debug(f"Editing fit name to {name}.")
|
||||||
|
fit.name = name
|
||||||
|
|
||||||
|
def to_json(self) -> dict:
|
||||||
|
"""Converts the measurement to a JSON-compatible format.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
dict: The measurement in JSON-compatible format.
|
||||||
|
"""
|
||||||
|
return {
|
||||||
|
"name": self.name,
|
||||||
|
"tdx": self.tdx.tolist(),
|
||||||
|
"tdy": [[x.real, x.imag] for x in self.tdy],
|
||||||
|
"target_frequency": self.target_frequency,
|
||||||
|
"IF_frequency": self.IF_frequency,
|
||||||
|
"fits": [fit.to_json() for fit in self.fits],
|
||||||
|
}
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
def from_json(cls, json: dict) -> "Measurement":
|
||||||
|
"""Converts the JSON format to a measurement.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
json (dict): The measurement in JSON-compatible format.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Measurement: The measurement.
|
||||||
|
"""
|
||||||
|
tdy = np.array([complex(y[0], y[1]) for y in json["tdy"]])
|
||||||
|
measurement = cls(
|
||||||
|
json["name"],
|
||||||
|
np.array(json["tdx"]),
|
||||||
|
tdy,
|
||||||
|
target_frequency=json["target_frequency"],
|
||||||
|
IF_frequency=json["IF_frequency"],
|
||||||
|
)
|
||||||
|
|
||||||
|
for fit_json in json["fits"]:
|
||||||
|
measurement.add_fit(Fit.from_json(fit_json, measurement))
|
||||||
|
|
||||||
|
return measurement
|
||||||
|
|
||||||
|
# Properties for encapsulation
|
||||||
|
@property
|
||||||
|
def name(self) -> str:
|
||||||
|
"""Name of the measurement."""
|
||||||
|
return self._name
|
||||||
|
|
||||||
|
@name.setter
|
||||||
|
def name(self, value: str) -> None:
|
||||||
|
self._name = value
|
||||||
|
|
||||||
|
@property
|
||||||
|
def tdx(self) -> np.array:
|
||||||
|
"""Time domain data for the measurement (x)."""
|
||||||
|
return self._tdx
|
||||||
|
|
||||||
|
@tdx.setter
|
||||||
|
def tdx(self, value: np.array) -> None:
|
||||||
|
self._tdx = value
|
||||||
|
|
||||||
|
@property
|
||||||
|
def tdy(self) -> np.array:
|
||||||
|
"""Time domain data for the measurement (y)."""
|
||||||
|
return self._tdy
|
||||||
|
|
||||||
|
@tdy.setter
|
||||||
|
def tdy(self, value: np.array) -> None:
|
||||||
|
self._tdy = value
|
||||||
|
|
||||||
|
@property
|
||||||
|
def fdx(self) -> np.array:
|
||||||
|
"""Frequency domain data for the measurement (x)."""
|
||||||
|
return self._fdx
|
||||||
|
|
||||||
|
@fdx.setter
|
||||||
|
def fdx(self, value: np.array) -> None:
|
||||||
|
self._fdx = value
|
||||||
|
|
||||||
|
@property
|
||||||
|
def fdy(self) -> np.array:
|
||||||
|
"""Frequency domain data for the measurement (y)."""
|
||||||
|
return self._fdy
|
||||||
|
|
||||||
|
@fdy.setter
|
||||||
|
def fdy(self, value: np.array) -> None:
|
||||||
|
self._fdy = value
|
||||||
|
|
||||||
|
@property
|
||||||
|
def target_frequency(self) -> float:
|
||||||
|
"""Target frequency of the measurement."""
|
||||||
|
return self._target_frequency
|
||||||
|
|
||||||
|
@target_frequency.setter
|
||||||
|
def target_frequency(self, value: float) -> None:
|
||||||
|
self._target_frequency = value
|
||||||
|
|
||||||
|
@property
|
||||||
|
def fits(self) -> list:
|
||||||
|
"""Fits of the measurement."""
|
||||||
|
return self._fits
|
||||||
|
|
||||||
|
@fits.setter
|
||||||
|
def fits(self, value: list) -> None:
|
||||||
|
self._fits = value
|
||||||
|
|
||||||
|
|
||||||
|
class Fit:
|
||||||
|
"""The fit class for measurement data. A fit can be performed on either the frequency or time domain data.
|
||||||
|
|
||||||
|
A measurement can have multiple fits.
|
||||||
|
"""
|
||||||
|
|
||||||
|
subclasses = []
|
||||||
|
|
||||||
|
def __init_subclass__(cls, **kwargs) -> None:
|
||||||
|
"""Adds the subclass to the list of subclasses."""
|
||||||
|
super().__init_subclass__(**kwargs)
|
||||||
|
cls.subclasses.append(cls)
|
||||||
|
|
||||||
|
def __init__(self, name: str, domain: str, measurement: Measurement) -> None:
|
||||||
|
"""Initializes the fit."""
|
||||||
|
self.name = name
|
||||||
|
self.domain = domain
|
||||||
|
self.measurement = measurement
|
||||||
|
self.fit()
|
||||||
|
|
||||||
|
def fit(self) -> None:
|
||||||
|
"""Fits the measurement data and sets the fit parameters and covariance."""
|
||||||
|
if self.domain == "time":
|
||||||
|
x = self.measurement.tdx
|
||||||
|
y = self.measurement.tdy
|
||||||
|
elif self.domain == "frequency":
|
||||||
|
x = self.measurement.fdx
|
||||||
|
y = self.measurement.fdy
|
||||||
|
else:
|
||||||
|
raise ValueError("Domain not recognized.")
|
||||||
|
|
||||||
|
initial_guess = self.initial_guess()
|
||||||
|
self.parameters, self.covariance = curve_fit(
|
||||||
|
self.fit_function, x, abs(y), p0=initial_guess
|
||||||
|
)
|
||||||
|
|
||||||
|
self.x = x
|
||||||
|
self.y = self.fit_function(x, *self.parameters)
|
||||||
|
|
||||||
|
def fit_function(self, x: np.array, *parameters) -> np.array:
|
||||||
|
"""The fit function.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
x (np.array): The x data.
|
||||||
|
*parameters: The fit parameters.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
np.array: The y data.
|
||||||
|
"""
|
||||||
|
raise NotImplementedError
|
||||||
|
|
||||||
|
def initial_guess(self) -> list:
|
||||||
|
"""Initial guess for the fit.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
list: The initial guess.
|
||||||
|
"""
|
||||||
|
raise NotImplementedError
|
||||||
|
|
||||||
|
def to_json(self) -> dict:
|
||||||
|
"""Converts the fit to a JSON-compatible format.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
dict: The fit in JSON-compatible format.
|
||||||
|
"""
|
||||||
|
return {
|
||||||
|
"name": self.name,
|
||||||
|
"class": self.__class__.__name__,
|
||||||
|
}
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
def from_json(cls, data: dict, measurement: Measurement) -> "Fit":
|
||||||
|
"""Converts the JSON format to a fit.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
data (dict): The fit in JSON-compatible format.
|
||||||
|
measurement (Measurement): The measurement.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Fit: The fit.
|
||||||
|
"""
|
||||||
|
for subclass in cls.subclasses:
|
||||||
|
if subclass.__name__ == data["class"]:
|
||||||
|
return subclass(name=data["name"], measurement=measurement)
|
||||||
|
|
||||||
|
raise ValueError(f"Subclass {data['class']} not found.")
|
||||||
|
|
||||||
|
@property
|
||||||
|
def x(self) -> np.array:
|
||||||
|
"""The x data of the fit."""
|
||||||
|
return self._x
|
||||||
|
|
||||||
|
@x.setter
|
||||||
|
def x(self, value: np.array) -> None:
|
||||||
|
self._x = value
|
||||||
|
|
||||||
|
@property
|
||||||
|
def y(self) -> np.array:
|
||||||
|
"""The y data of the fit."""
|
||||||
|
return self._y
|
||||||
|
|
||||||
|
@y.setter
|
||||||
|
def y(self, value: np.array) -> None:
|
||||||
|
self._y = value
|
||||||
|
|
||||||
|
|
||||||
|
class T2StarFit(Fit):
|
||||||
|
"""T2* fit for measurement data."""
|
||||||
|
|
||||||
|
def __init__(self, measurement: Measurement, name: str = "T2*") -> None:
|
||||||
|
"""Initializes the T2* fit."""
|
||||||
|
super().__init__(name, "time", measurement)
|
||||||
|
|
||||||
|
def fit(self) -> None:
|
||||||
|
"""Fits the measurement data and sets the fit parameters and covariance."""
|
||||||
|
super().fit()
|
||||||
|
self.parameters = {
|
||||||
|
"S0": self.parameters[0],
|
||||||
|
"T2Star": self.parameters[1],
|
||||||
|
"covariance": self.covariance,
|
||||||
|
}
|
||||||
|
|
||||||
|
def fit_function(self, t: np.array, S0: float, T2Star: float) -> np.array:
|
||||||
|
"""The T2* fit function used for curve fitting."""
|
||||||
|
return S0 * np.exp(-t / T2Star)
|
||||||
|
|
||||||
|
def initial_guess(self) -> list:
|
||||||
|
"""Initial guess for the T2* fit."""
|
||||||
|
return [1, 1]
|
||||||
|
|
||||||
|
class LorentzianFit(Fit):
|
||||||
|
"""Lorentzian fit for measurement data."""
|
||||||
|
|
||||||
|
def __init__(self, measurement: Measurement, name: str = "Lorentzian") -> None:
|
||||||
|
"""Initializes the Lorentzian fit."""
|
||||||
|
super().__init__(name, "frequency", measurement)
|
||||||
|
|
||||||
|
def fit(self) -> None:
|
||||||
|
"""Fits the measurement data and sets the fit parameters and covariance."""
|
||||||
|
super().fit()
|
||||||
|
self.parameters = {
|
||||||
|
"S0": self.parameters[0],
|
||||||
|
"T2Star": self.parameters[1],
|
||||||
|
"covariance": self.covariance,
|
||||||
|
}
|
||||||
|
logger.debug("Lorentzian fit parameters: %s", self.parameters)
|
||||||
|
|
||||||
|
def fit_function(self, f: np.array, S0: float, T2Star: float) -> np.array:
|
||||||
|
"""The Lorentzian fit function used for curve fitting."""
|
||||||
|
return S0 / (1 + (2 * np.pi * f * T2Star) ** 2)
|
||||||
|
|
||||||
|
def initial_guess(self) -> list:
|
||||||
|
"""Initial guess for the Lorentzian fit."""
|
||||||
|
return [1, 1]
|
229
src/quackseq/options.py
Normal file
229
src/quackseq/options.py
Normal file
|
@ -0,0 +1,229 @@
|
||||||
|
import logging
|
||||||
|
from quackseq.functions import Function
|
||||||
|
|
||||||
|
logger = logging.getLogger(__name__)
|
||||||
|
|
||||||
|
class Option:
|
||||||
|
"""Defines options for the pulse parameters which can then be set accordingly.
|
||||||
|
|
||||||
|
Options can be of different types, for example boolean, numeric or function.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
name (str): The name of the option.
|
||||||
|
value: The value of the option.
|
||||||
|
|
||||||
|
Attributes:
|
||||||
|
name (str): The name of the option.
|
||||||
|
value: The value of the option.
|
||||||
|
"""
|
||||||
|
|
||||||
|
subclasses = []
|
||||||
|
|
||||||
|
def __init_subclass__(cls, **kwargs):
|
||||||
|
"""Adds the subclass to the list of subclasses."""
|
||||||
|
super().__init_subclass__(**kwargs)
|
||||||
|
cls.subclasses.append(cls)
|
||||||
|
|
||||||
|
def __init__(self, name: str, value) -> None:
|
||||||
|
"""Initializes the option."""
|
||||||
|
self.name = name
|
||||||
|
self.value = value
|
||||||
|
|
||||||
|
def set_value(self):
|
||||||
|
"""Sets the value of the option.
|
||||||
|
|
||||||
|
This method has to be implemented in the derived classes.
|
||||||
|
"""
|
||||||
|
raise NotImplementedError
|
||||||
|
|
||||||
|
def to_json(self):
|
||||||
|
"""Returns a json representation of the option.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
dict: The json representation of the option.
|
||||||
|
"""
|
||||||
|
return {
|
||||||
|
"name": self.name,
|
||||||
|
"value": self.value,
|
||||||
|
"class": self.__class__.__name__,
|
||||||
|
}
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
def from_json(cls, data) -> "Option":
|
||||||
|
"""Creates an option from a json representation.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
data (dict): The json representation of the option.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Option: The option.
|
||||||
|
"""
|
||||||
|
for subclass in cls.subclasses:
|
||||||
|
logger.debug(f"Keys data: {data.keys()}")
|
||||||
|
if subclass.__name__ == data["class"]:
|
||||||
|
cls = subclass
|
||||||
|
break
|
||||||
|
|
||||||
|
# Check if from_json is implemented for the subclass
|
||||||
|
if cls.from_json.__func__ == Option.from_json.__func__:
|
||||||
|
obj = cls(data["name"], data["value"])
|
||||||
|
else:
|
||||||
|
obj = cls.from_json(data)
|
||||||
|
|
||||||
|
return obj
|
||||||
|
|
||||||
|
|
||||||
|
class BooleanOption(Option):
|
||||||
|
"""Defines a boolean option for a pulse parameter option."""
|
||||||
|
|
||||||
|
def set_value(self, value):
|
||||||
|
"""Sets the value of the option."""
|
||||||
|
self.value = value
|
||||||
|
self.value_changed.emit()
|
||||||
|
|
||||||
|
|
||||||
|
class NumericOption(Option):
|
||||||
|
"""Defines a numeric option for a pulse parameter option."""
|
||||||
|
|
||||||
|
def __init__(
|
||||||
|
self,
|
||||||
|
name: str,
|
||||||
|
value,
|
||||||
|
is_float=True,
|
||||||
|
min_value=None,
|
||||||
|
max_value=None,
|
||||||
|
slider=False,
|
||||||
|
) -> None:
|
||||||
|
"""Initializes the NumericOption.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
name (str): The name of the option.
|
||||||
|
value: The value of the option.
|
||||||
|
is_float (bool): If the value is a float.
|
||||||
|
min_value: The minimum value of the option.
|
||||||
|
max_value: The maximum value of the option.
|
||||||
|
"""
|
||||||
|
super().__init__(name, value)
|
||||||
|
self.is_float = is_float
|
||||||
|
self.min_value = min_value
|
||||||
|
self.max_value = max_value
|
||||||
|
|
||||||
|
def set_value(self, value):
|
||||||
|
"""Sets the value of the option."""
|
||||||
|
if value < self.min_value:
|
||||||
|
self.value = self.min_value
|
||||||
|
self.value_changed.emit()
|
||||||
|
elif value >= self.max_value:
|
||||||
|
self.value = self.max_value
|
||||||
|
self.value_changed.emit()
|
||||||
|
else:
|
||||||
|
raise ValueError(
|
||||||
|
f"Value {value} is not in the range of {self.min_value} to {self.max_value}. This should have been caught earlier."
|
||||||
|
)
|
||||||
|
|
||||||
|
def to_json(self):
|
||||||
|
"""Returns a json representation of the option.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
dict: The json representation of the option.
|
||||||
|
"""
|
||||||
|
return {
|
||||||
|
"name": self.name,
|
||||||
|
"value": self.value,
|
||||||
|
"class": self.__class__.__name__,
|
||||||
|
"is_float": self.is_float,
|
||||||
|
"min_value": self.min_value,
|
||||||
|
"max_value": self.max_value,
|
||||||
|
}
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
def from_json(cls, data):
|
||||||
|
"""Creates a NumericOption from a json representation.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
data (dict): The json representation of the NumericOption.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
NumericOption: The NumericOption.
|
||||||
|
"""
|
||||||
|
obj = cls(
|
||||||
|
data["name"],
|
||||||
|
data["value"],
|
||||||
|
is_float=data["is_float"],
|
||||||
|
min_value=data["min_value"],
|
||||||
|
max_value=data["max_value"],
|
||||||
|
)
|
||||||
|
return obj
|
||||||
|
|
||||||
|
|
||||||
|
class FunctionOption(Option):
|
||||||
|
"""Defines a selection option for a pulse parameter option.
|
||||||
|
|
||||||
|
It takes different function objects.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
name (str): The name of the option.
|
||||||
|
functions (list): The functions that can be selected.
|
||||||
|
|
||||||
|
Attributes:
|
||||||
|
name (str): The name of the option.
|
||||||
|
functions (list): The functions that can be selected.
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, name, functions) -> None:
|
||||||
|
"""Initializes the FunctionOption."""
|
||||||
|
super().__init__(name, functions[0])
|
||||||
|
self.functions = functions
|
||||||
|
|
||||||
|
def set_value(self, value):
|
||||||
|
"""Sets the value of the option.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
value: The value of the option.
|
||||||
|
"""
|
||||||
|
self.value = value
|
||||||
|
self.value_changed.emit()
|
||||||
|
|
||||||
|
def get_function_by_name(self, name):
|
||||||
|
"""Returns the function with the given name.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
name (str): The name of the function.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Function: The function with the given name.
|
||||||
|
"""
|
||||||
|
for function in self.functions:
|
||||||
|
if function.name == name:
|
||||||
|
return function
|
||||||
|
raise ValueError(f"Function with name {name} not found")
|
||||||
|
|
||||||
|
def to_json(self):
|
||||||
|
"""Returns a json representation of the option.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
dict: The json representation of the option.
|
||||||
|
"""
|
||||||
|
return {
|
||||||
|
"name": self.name,
|
||||||
|
"value": self.value.to_json(),
|
||||||
|
"class": self.__class__.__name__,
|
||||||
|
"functions": [function.to_json() for function in self.functions],
|
||||||
|
}
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
def from_json(cls, data):
|
||||||
|
"""Creates a FunctionOption from a json representation.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
data (dict): The json representation of the FunctionOption.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
FunctionOption: The FunctionOption.
|
||||||
|
"""
|
||||||
|
logger.debug(f"Data: {data}")
|
||||||
|
# These are all available functions
|
||||||
|
functions = [Function.from_json(function) for function in data["functions"]]
|
||||||
|
obj = cls(data["name"], functions)
|
||||||
|
obj.value = Function.from_json(data["value"])
|
||||||
|
return obj
|
165
src/quackseq/pulseparameters.py
Normal file
165
src/quackseq/pulseparameters.py
Normal file
|
@ -0,0 +1,165 @@
|
||||||
|
"""Contains the classes for the pulse parameters of the spectrometer. It includes the functions and the options for the pulse parameters.
|
||||||
|
|
||||||
|
Todo:
|
||||||
|
* This shouldn't be in the spectrometer module. It should be in it"s own pulse sequence module.
|
||||||
|
"""
|
||||||
|
|
||||||
|
from __future__ import annotations
|
||||||
|
import logging
|
||||||
|
|
||||||
|
from numpy.core.multiarray import array as array
|
||||||
|
|
||||||
|
from quackseq.options import BooleanOption, FunctionOption, NumericOption, Option
|
||||||
|
from quackseq.functions import RectFunction, SincFunction, GaussianFunction, CustomFunction
|
||||||
|
|
||||||
|
logger = logging.getLogger(__name__)
|
||||||
|
|
||||||
|
|
||||||
|
class PulseParameter:
|
||||||
|
"""A pulse parameter is a value that can be different for each event in a pulse sequence.
|
||||||
|
|
||||||
|
E.g. the transmit pulse power or the phase of the transmit pulse.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
name (str) : The name of the pulse parameter
|
||||||
|
|
||||||
|
Attributes:
|
||||||
|
name (str) : The name of the pulse parameter
|
||||||
|
options (OrderedDict) : The options of the pulse parameter
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, name: str):
|
||||||
|
"""Initializes the pulse parameter.
|
||||||
|
|
||||||
|
Arguments:
|
||||||
|
name (str) : The name of the pulse parameter
|
||||||
|
"""
|
||||||
|
self.name = name
|
||||||
|
self.options = list()
|
||||||
|
|
||||||
|
def add_option(self, option: Option) -> None:
|
||||||
|
"""Adds an option to the pulse parameter.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
option (Option) : The option to add
|
||||||
|
"""
|
||||||
|
self.options.append(option)
|
||||||
|
|
||||||
|
def get_options(self) -> list:
|
||||||
|
"""Gets the options of the pulse parameter.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
list : The options of the pulse parameter
|
||||||
|
"""
|
||||||
|
return self.options
|
||||||
|
|
||||||
|
def get_option_by_name(self, name: str) -> Option:
|
||||||
|
"""Gets an option by its name.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
name (str) : The name of the option
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Option : The option with the specified name
|
||||||
|
|
||||||
|
Raises:
|
||||||
|
ValueError : If no option with the specified name is found
|
||||||
|
"""
|
||||||
|
for option in self.options:
|
||||||
|
if option.name == name:
|
||||||
|
return option
|
||||||
|
raise ValueError(f"Option with name {name} not found")
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
class TXPulse(PulseParameter):
|
||||||
|
"""Basic TX Pulse Parameter. It includes options for the relative amplitude, the phase and the pulse shape.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
name (str): The name of the pulse parameter.
|
||||||
|
"""
|
||||||
|
|
||||||
|
RELATIVE_AMPLITUDE = "Relative TX Amplitude (%)"
|
||||||
|
TX_PHASE = "TX Phase"
|
||||||
|
TX_PULSE_SHAPE = "TX Pulse Shape"
|
||||||
|
N_PHASE_CYCLES = "Number of Phase Cycles"
|
||||||
|
PHASE_CYCLE_LEVEL = "Phase Cycle Level"
|
||||||
|
|
||||||
|
def __init__(self, name: str) -> None:
|
||||||
|
"""Initializes the TX Pulse Parameter.
|
||||||
|
|
||||||
|
It adds the options for the relative amplitude, the phase and the pulse shape.
|
||||||
|
"""
|
||||||
|
super().__init__(name)
|
||||||
|
self.add_option(
|
||||||
|
NumericOption(
|
||||||
|
self.RELATIVE_AMPLITUDE,
|
||||||
|
0,
|
||||||
|
is_float=False,
|
||||||
|
min_value=0,
|
||||||
|
max_value=100,
|
||||||
|
slider=True,
|
||||||
|
)
|
||||||
|
)
|
||||||
|
self.add_option(NumericOption(self.TX_PHASE, 0))
|
||||||
|
self.add_option(
|
||||||
|
NumericOption(
|
||||||
|
self.N_PHASE_CYCLES, 1, is_float=False, min_value=1, max_value=360
|
||||||
|
)
|
||||||
|
)
|
||||||
|
self.add_option(
|
||||||
|
NumericOption(
|
||||||
|
self.PHASE_CYCLE_LEVEL, 0, is_float=False, min_value=0, max_value=10
|
||||||
|
)
|
||||||
|
)
|
||||||
|
self.add_option(
|
||||||
|
FunctionOption(
|
||||||
|
self.TX_PULSE_SHAPE,
|
||||||
|
[
|
||||||
|
RectFunction(),
|
||||||
|
SincFunction(),
|
||||||
|
GaussianFunction(),
|
||||||
|
CustomFunction(),
|
||||||
|
],
|
||||||
|
),
|
||||||
|
)
|
||||||
|
|
||||||
|
class RXReadout(PulseParameter):
|
||||||
|
"""Basic PulseParameter for the RX Readout. It includes an option for the RX Readout state.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
name (str): The name of the pulse parameter.
|
||||||
|
|
||||||
|
Attributes:
|
||||||
|
RX (str): The RX Readout state.
|
||||||
|
"""
|
||||||
|
|
||||||
|
RX = "RX"
|
||||||
|
|
||||||
|
def __init__(self, name) -> None:
|
||||||
|
"""Initializes the RX Readout PulseParameter.
|
||||||
|
|
||||||
|
It adds an option for the RX Readout state.
|
||||||
|
"""
|
||||||
|
super().__init__(name)
|
||||||
|
self.add_option(BooleanOption(self.RX, False))
|
||||||
|
|
||||||
|
class Gate(PulseParameter):
|
||||||
|
"""Basic PulseParameter for the Gate. It includes an option for the Gate state.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
name (str): The name of the pulse parameter.
|
||||||
|
|
||||||
|
Attributes:
|
||||||
|
GATE_STATE (str): The Gate state.
|
||||||
|
"""
|
||||||
|
|
||||||
|
GATE_STATE = "Gate State"
|
||||||
|
|
||||||
|
def __init__(self, name) -> None:
|
||||||
|
"""Initializes the Gate PulseParameter.
|
||||||
|
|
||||||
|
It adds an option for the Gate state.
|
||||||
|
"""
|
||||||
|
super().__init__(name)
|
||||||
|
self.add_option(BooleanOption(self.GATE_STATE, False))
|
165
src/quackseq/pulsesequence.py
Normal file
165
src/quackseq/pulsesequence.py
Normal file
|
@ -0,0 +1,165 @@
|
||||||
|
"""Contains the PulseSequence class that is used to store a pulse sequence and its events."""
|
||||||
|
|
||||||
|
import logging
|
||||||
|
import importlib.metadata
|
||||||
|
|
||||||
|
logger = logging.getLogger(__name__)
|
||||||
|
|
||||||
|
|
||||||
|
class PulseSequence:
|
||||||
|
"""A pulse sequence is a collection of events that are executed in a certain order.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
name (str): The name of the pulse sequence
|
||||||
|
|
||||||
|
Attributes:
|
||||||
|
name (str): The name of the pulse sequence
|
||||||
|
events (list): The events of the pulse sequence
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, name, version = None) -> None:
|
||||||
|
"""Initializes the pulse sequence."""
|
||||||
|
self.name = name
|
||||||
|
# Saving version to check for compatability of saved sequence
|
||||||
|
if version is not None:
|
||||||
|
self.version = version
|
||||||
|
else:
|
||||||
|
self.version = importlib.metadata.version("nqrduck_spectrometer")
|
||||||
|
self.events = list()
|
||||||
|
|
||||||
|
def get_event_names(self) -> list:
|
||||||
|
"""Returns a list of the names of the events in the pulse sequence.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
list: The names of the events
|
||||||
|
"""
|
||||||
|
return [event.name for event in self.events]
|
||||||
|
|
||||||
|
def add_event(self, event_name: str, duration: float) -> None:
|
||||||
|
"""Add a new event to the pulse sequence.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
event_name (str): The name of the event
|
||||||
|
duration (float): The duration of the event
|
||||||
|
"""
|
||||||
|
self.events.append(self.Event(event_name, f"{float(duration):.16g}u"))
|
||||||
|
|
||||||
|
|
||||||
|
def delete_event(self, event_name: str) -> None:
|
||||||
|
"""Deletes an event from the pulse sequence.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
event_name (str): The name of the event to delete
|
||||||
|
"""
|
||||||
|
for event in self.events:
|
||||||
|
if event.name == event_name:
|
||||||
|
self.events.remove(event)
|
||||||
|
break
|
||||||
|
|
||||||
|
def to_json(self):
|
||||||
|
"""Returns a dict with all the data in the pulse sequence.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
dict: The dict with the sequence data
|
||||||
|
"""
|
||||||
|
# Get the versions of this package
|
||||||
|
data = {"name": self.name, "version" : self.version, "events": []}
|
||||||
|
for event in self.events:
|
||||||
|
event_data = {
|
||||||
|
"name": event.name,
|
||||||
|
"duration": event.duration,
|
||||||
|
"parameters": [],
|
||||||
|
}
|
||||||
|
for parameter in event.parameters.keys():
|
||||||
|
event_data["parameters"].append({"name": parameter, "value": []})
|
||||||
|
for option in event.parameters[parameter].options:
|
||||||
|
event_data["parameters"][-1]["value"].append(option.to_json())
|
||||||
|
data["events"].append(event_data)
|
||||||
|
return data
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
def load_sequence(cls, sequence, pulse_parameter_options):
|
||||||
|
"""Loads a pulse sequence from a dict.
|
||||||
|
|
||||||
|
The pulse paramter options are needed to load the parameters
|
||||||
|
and make sure the correct spectrometer is active.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
sequence (dict): The dict with the sequence data
|
||||||
|
pulse_parameter_options (dict): The dict with the pulse parameter options
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
PulseSequence: The loaded pulse sequence
|
||||||
|
|
||||||
|
Raises:
|
||||||
|
KeyError: If the pulse parameter options are not the same as the ones in the pulse sequence
|
||||||
|
"""
|
||||||
|
try:
|
||||||
|
obj = cls(sequence["name"], version = sequence["version"])
|
||||||
|
except KeyError:
|
||||||
|
logger.error("Pulse sequence version not found")
|
||||||
|
raise KeyError("Pulse sequence version not found")
|
||||||
|
|
||||||
|
for event_data in sequence["events"]:
|
||||||
|
obj.events.append(cls.Event.load_event(event_data, pulse_parameter_options))
|
||||||
|
|
||||||
|
return obj
|
||||||
|
|
||||||
|
class Variable:
|
||||||
|
"""A variable is a parameter that can be used within a pulsesequence as a placeholder.
|
||||||
|
|
||||||
|
For example the event duration a Variable with name a can be set. This variable can then be set to a list of different values.
|
||||||
|
On execution of the pulse sequence the event duration will be set to the first value in the list.
|
||||||
|
Then the pulse sequence will be executed with the second value of the list. This is repeated until the pulse sequence has
|
||||||
|
been executed with all values in the list.
|
||||||
|
"""
|
||||||
|
|
||||||
|
@property
|
||||||
|
def name(self):
|
||||||
|
"""The name of the variable."""
|
||||||
|
return self._name
|
||||||
|
|
||||||
|
@name.setter
|
||||||
|
def name(self, name: str):
|
||||||
|
if not isinstance(name, str):
|
||||||
|
raise TypeError("Name needs to be a string")
|
||||||
|
self._name = name
|
||||||
|
|
||||||
|
@property
|
||||||
|
def values(self):
|
||||||
|
"""The values of the variable. This is a list of values that the variable can take."""
|
||||||
|
return self._values
|
||||||
|
|
||||||
|
@values.setter
|
||||||
|
def values(self, values: list):
|
||||||
|
if not isinstance(values, list):
|
||||||
|
raise TypeError("Values needs to be a list")
|
||||||
|
self._values = values
|
||||||
|
|
||||||
|
class VariableGroup:
|
||||||
|
"""Variables can be grouped together.
|
||||||
|
|
||||||
|
If we have groups a and b the pulse sequence will be executed for all combinations of variables in a and b.
|
||||||
|
"""
|
||||||
|
|
||||||
|
@property
|
||||||
|
def name(self):
|
||||||
|
"""The name of the variable group."""
|
||||||
|
return self._name
|
||||||
|
|
||||||
|
@name.setter
|
||||||
|
def name(self, name: str):
|
||||||
|
if not isinstance(name, str):
|
||||||
|
raise TypeError("Name needs to be a string")
|
||||||
|
self._name = name
|
||||||
|
|
||||||
|
@property
|
||||||
|
def variables(self):
|
||||||
|
"""The variables in the group. This is a list of variables."""
|
||||||
|
return self._variables
|
||||||
|
|
||||||
|
@variables.setter
|
||||||
|
def variables(self, variables: list):
|
||||||
|
if not isinstance(variables, list):
|
||||||
|
raise TypeError("Variables needs to be a list")
|
||||||
|
self._variables = variables
|
86
src/quackseq/signalprocessing.py
Normal file
86
src/quackseq/signalprocessing.py
Normal file
|
@ -0,0 +1,86 @@
|
||||||
|
"""Helper used for signal processing."""
|
||||||
|
import logging
|
||||||
|
from scipy.fft import fft, fftfreq, fftshift
|
||||||
|
import numpy as np
|
||||||
|
import sympy
|
||||||
|
|
||||||
|
logger = logging.getLogger(__name__)
|
||||||
|
|
||||||
|
class SignalProcessing:
|
||||||
|
"""This class provides various signal processing methods that can then be used by nqrduck modules."""
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
def fft(cls, tdx : np.array, tdy: np.array, freq_shift : float = 0, zero_padding = 1000) -> tuple[np.array, np.array]:
|
||||||
|
"""This method calculates the FFT of the time domain data.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
tdx (np.array): Time domain x data in seconds.
|
||||||
|
tdy (np.array): Time domain magnitude y data.
|
||||||
|
freq_shift (float): Frequency shift in MHz - this can be useful if the spectrometer has it's frequency data in the IF band.
|
||||||
|
zero_padding (float): Zero padding to be used in the FFT.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
np.array: Frequency domain x data in MHz.
|
||||||
|
np.array: Frequency domain magnitude y data.
|
||||||
|
"""
|
||||||
|
dwell_time = (tdx[1] - tdx[0])
|
||||||
|
|
||||||
|
N = len(tdx) + zero_padding
|
||||||
|
|
||||||
|
if freq_shift != 0:
|
||||||
|
# Create the complex exponential to shift the frequency
|
||||||
|
shift_signal = np.exp(-2j * np.pi * freq_shift * tdx)[:, np.newaxis]
|
||||||
|
|
||||||
|
# Apply the shift by multiplying the time domain signal
|
||||||
|
tdy_shift = np.abs(tdy * shift_signal)
|
||||||
|
ydf = fftshift(fft(tdy_shift, N, axis=0), axes=0)
|
||||||
|
|
||||||
|
else:
|
||||||
|
ydf = fftshift(fft(tdy, N, axis=0), axes=0)
|
||||||
|
|
||||||
|
xdf = fftshift(fftfreq(N, dwell_time))
|
||||||
|
|
||||||
|
return xdf, ydf
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
def baseline_correction(cls, fdx : np.array, fdy : np.array, order : int) -> np.array:
|
||||||
|
"""This method calculates the baseline correction of the frequency domain data.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
fdx (np.array): Frequency domain x data in MHz.
|
||||||
|
fdy (np.array): Frequency domain magnitude y data.
|
||||||
|
order (int): Order of the polynomial used for baseline correction.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
np.array: Frequency domain magnitude y data with baseline correction.
|
||||||
|
"""
|
||||||
|
pass
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
def apodization(cls, tdx : np.array, tdy : np.array, apodization_function : sympy.Expr) -> np.array:
|
||||||
|
"""This method calculates the apodization of the time domain data.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
tdx (np.array): Time domain x data in seconds.
|
||||||
|
tdy (np.array): Time domain magnitude y data.
|
||||||
|
apodization_function (sympy.Expr): Apodization function.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
np.array: Time domain magnitude y data with apodization.
|
||||||
|
"""
|
||||||
|
weight = np.array([apodization_function.subs("t", t) for t in tdx])
|
||||||
|
return tdy * weight
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
def peak_picking(cls, fdx: np.array, fdy: np.array, threshold : float = 0.05) -> tuple[np.array, np.array]:
|
||||||
|
"""This method calculates the peak picking of the frequency domain data.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
fdx (np.array): Frequency domain x data in MHz.
|
||||||
|
fdy (np.array): Frequency domain magnitude y data.
|
||||||
|
threshold (float): Threshold for peak picking.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
list: x,y data of the peaks.
|
||||||
|
"""
|
||||||
|
pass
|
92
src/quackseq/spectrometer/spectrometer_model.py
Normal file
92
src/quackseq/spectrometer/spectrometer_model.py
Normal file
|
@ -0,0 +1,92 @@
|
||||||
|
"""The base class for all spectrometer models."""
|
||||||
|
|
||||||
|
import logging
|
||||||
|
from collections import OrderedDict
|
||||||
|
from quackseq.spectrometer.spectrometer_setting import Setting
|
||||||
|
|
||||||
|
logger = logging.getLogger(__name__)
|
||||||
|
|
||||||
|
|
||||||
|
class SpectrometerModel():
|
||||||
|
"""The base class for all spectrometer models.
|
||||||
|
|
||||||
|
It contains the settings and pulse parameters of the spectrometer.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
module (Module) : The module that the spectrometer is connected to
|
||||||
|
|
||||||
|
Attributes:
|
||||||
|
settings (OrderedDict) : The settings of the spectrometer
|
||||||
|
pulse_parameter_options (OrderedDict) : The pulse parameter options of the spectrometer
|
||||||
|
"""
|
||||||
|
|
||||||
|
settings: OrderedDict
|
||||||
|
pulse_parameter_options: OrderedDict
|
||||||
|
|
||||||
|
def __init__(self, module):
|
||||||
|
"""Initializes the spectrometer model.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
module (Module) : The module that the spectrometer is connected to
|
||||||
|
"""
|
||||||
|
super().__init__(module)
|
||||||
|
self.settings = OrderedDict()
|
||||||
|
self.pulse_parameter_options = OrderedDict()
|
||||||
|
|
||||||
|
def add_setting(self, setting: Setting, category: str) -> None:
|
||||||
|
"""Adds a setting to the spectrometer.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
setting (Setting) : The setting to add
|
||||||
|
category (str) : The category of the setting
|
||||||
|
"""
|
||||||
|
if category not in self.settings.keys():
|
||||||
|
self.settings[category] = []
|
||||||
|
self.settings[category].append(setting)
|
||||||
|
|
||||||
|
def get_setting_by_name(self, name: str) -> Setting:
|
||||||
|
"""Gets a setting by its name.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
name (str) : The name of the setting
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Setting : The setting with the specified name
|
||||||
|
|
||||||
|
Raises:
|
||||||
|
ValueError : If no setting with the specified name is found
|
||||||
|
"""
|
||||||
|
for category in self.settings.keys():
|
||||||
|
for setting in self.settings[category]:
|
||||||
|
if setting.name == name:
|
||||||
|
return setting
|
||||||
|
raise ValueError(f"Setting with name {name} not found")
|
||||||
|
|
||||||
|
def add_pulse_parameter_option(
|
||||||
|
self, name: str, pulse_parameter_class: "PulseParameter"
|
||||||
|
) -> None:
|
||||||
|
"""Adds a pulse parameter option to the spectrometer.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
name (str) : The name of the pulse parameter
|
||||||
|
pulse_parameter_class (PulseParameter) : The pulse parameter class
|
||||||
|
"""
|
||||||
|
self.pulse_parameter_options[name] = pulse_parameter_class
|
||||||
|
|
||||||
|
@property
|
||||||
|
def target_frequency(self):
|
||||||
|
"""The target frequency of the spectrometer in Hz. This is the frequency where the magnetic resonance experiment is performed."""
|
||||||
|
raise NotImplementedError
|
||||||
|
|
||||||
|
@target_frequency.setter
|
||||||
|
def target_frequency(self, value):
|
||||||
|
raise NotImplementedError
|
||||||
|
|
||||||
|
@property
|
||||||
|
def averages(self):
|
||||||
|
"""The number of averages for the spectrometer."""
|
||||||
|
raise NotImplementedError
|
||||||
|
|
||||||
|
@averages.setter
|
||||||
|
def averages(self, value):
|
||||||
|
raise NotImplementedError
|
243
src/quackseq/spectrometer/spectrometer_setting.py
Normal file
243
src/quackseq/spectrometer/spectrometer_setting.py
Normal file
|
@ -0,0 +1,243 @@
|
||||||
|
"""Settings for the different spectrometers."""
|
||||||
|
|
||||||
|
import logging
|
||||||
|
|
||||||
|
logger = logging.getLogger(__name__)
|
||||||
|
|
||||||
|
|
||||||
|
class Setting():
|
||||||
|
"""A setting for the spectrometer is a value that is the same for all events in a pulse sequence.
|
||||||
|
|
||||||
|
E.g. the Transmit gain or the number of points in a spectrum.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
name (str) : The name of the setting
|
||||||
|
description (str) : A description of the setting
|
||||||
|
default : The default value of the setting
|
||||||
|
|
||||||
|
Attributes:
|
||||||
|
name (str) : The name of the setting
|
||||||
|
description (str) : A description of the setting
|
||||||
|
value : The value of the setting
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, name: str, description: str = None, default=None) -> None:
|
||||||
|
"""Create a new setting.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
name (str): The name of the setting.
|
||||||
|
description (str): A description of the setting.
|
||||||
|
default: The default value of the setting.
|
||||||
|
"""
|
||||||
|
self.widget = None
|
||||||
|
super().__init__()
|
||||||
|
self.name = name
|
||||||
|
self.description = description
|
||||||
|
if default is not None:
|
||||||
|
self.value = default
|
||||||
|
# Update the description with the default value
|
||||||
|
self.description += f"\n (Default: {default})"
|
||||||
|
|
||||||
|
|
||||||
|
class NumericalSetting(Setting):
|
||||||
|
"""A setting that is a numerical value.
|
||||||
|
|
||||||
|
It can additionally have a minimum and maximum value.
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(
|
||||||
|
self, name: str, description: str, default, min_value=None, max_value=None
|
||||||
|
) -> None:
|
||||||
|
"""Create a new numerical setting."""
|
||||||
|
super().__init__(
|
||||||
|
name,
|
||||||
|
self.description_limit_info(description, min_value, max_value),
|
||||||
|
default,
|
||||||
|
)
|
||||||
|
|
||||||
|
def description_limit_info(self, description: str, min_value, max_value) -> str:
|
||||||
|
"""Updates the description with the limits of the setting if there are any.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
description (str): The description of the setting.
|
||||||
|
min_value: The minimum value of the setting.
|
||||||
|
max_value: The maximum value of the setting.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
str: The description of the setting with the limits.
|
||||||
|
"""
|
||||||
|
if min_value is not None and max_value is not None:
|
||||||
|
description += f"\n (min: {min_value}, max: {max_value})"
|
||||||
|
elif min_value is not None:
|
||||||
|
description += f"\n (min: {min_value})"
|
||||||
|
elif max_value is not None:
|
||||||
|
description += f"\n (max: {max_value})"
|
||||||
|
|
||||||
|
return description
|
||||||
|
|
||||||
|
|
||||||
|
class FloatSetting(NumericalSetting):
|
||||||
|
"""A setting that is a Float.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
name (str) : The name of the setting
|
||||||
|
default : The default value of the setting
|
||||||
|
description (str) : A description of the setting
|
||||||
|
min_value : The minimum value of the setting
|
||||||
|
max_value : The maximum value of the setting
|
||||||
|
"""
|
||||||
|
|
||||||
|
DEFAULT_LENGTH = 100
|
||||||
|
|
||||||
|
def __init__(
|
||||||
|
self,
|
||||||
|
name: str,
|
||||||
|
default: float,
|
||||||
|
description: str,
|
||||||
|
min_value: float = None,
|
||||||
|
max_value: float = None,
|
||||||
|
) -> None:
|
||||||
|
"""Create a new float setting."""
|
||||||
|
super().__init__(name, description, default, min_value, max_value)
|
||||||
|
|
||||||
|
@property
|
||||||
|
def value(self):
|
||||||
|
"""The value of the setting. In this case, a float."""
|
||||||
|
return self._value
|
||||||
|
|
||||||
|
@value.setter
|
||||||
|
def value(self, value):
|
||||||
|
logger.debug(f"Setting {self.name} to {value}")
|
||||||
|
self._value = float(value)
|
||||||
|
self.settings_changed.emit()
|
||||||
|
|
||||||
|
|
||||||
|
class IntSetting(NumericalSetting):
|
||||||
|
"""A setting that is an Integer.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
name (str) : The name of the setting
|
||||||
|
default : The default value of the setting
|
||||||
|
description (str) : A description of the setting
|
||||||
|
min_value : The minimum value of the setting
|
||||||
|
max_value : The maximum value of the setting
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(
|
||||||
|
self,
|
||||||
|
name: str,
|
||||||
|
default: int,
|
||||||
|
description: str,
|
||||||
|
min_value=None,
|
||||||
|
max_value=None,
|
||||||
|
spin_box: tuple = (False, False),
|
||||||
|
) -> None:
|
||||||
|
"""Create a new int setting."""
|
||||||
|
self.spin_box = spin_box
|
||||||
|
super().__init__(name, description, default, min_value, max_value)
|
||||||
|
|
||||||
|
@property
|
||||||
|
def value(self):
|
||||||
|
"""The value of the setting. In this case, an int."""
|
||||||
|
return self._value
|
||||||
|
|
||||||
|
@value.setter
|
||||||
|
def value(self, value):
|
||||||
|
logger.debug(f"Setting {self.name} to {value}")
|
||||||
|
value = int(float(value))
|
||||||
|
self._value = value
|
||||||
|
|
||||||
|
|
||||||
|
class BooleanSetting(Setting):
|
||||||
|
"""A setting that is a Boolean.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
name (str) : The name of the setting
|
||||||
|
default : The default value of the setting
|
||||||
|
description (str) : A description of the setting
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, name: str, default: bool, description: str) -> None:
|
||||||
|
"""Create a new boolean setting."""
|
||||||
|
super().__init__(name, description, default)
|
||||||
|
|
||||||
|
|
||||||
|
@property
|
||||||
|
def value(self):
|
||||||
|
"""The value of the setting. In this case, a bool."""
|
||||||
|
return self._value
|
||||||
|
|
||||||
|
@value.setter
|
||||||
|
def value(self, value):
|
||||||
|
try:
|
||||||
|
self._value = bool(value)
|
||||||
|
except ValueError:
|
||||||
|
raise ValueError("Value must be a bool")
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
class SelectionSetting(Setting):
|
||||||
|
"""A setting that is a selection from a list of options.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
name (str) : The name of the setting
|
||||||
|
options (list) : A list of options to choose from
|
||||||
|
default : The default value of the setting
|
||||||
|
description (str) : A description of the setting
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(
|
||||||
|
self, name: str, options: list, default: str, description: str
|
||||||
|
) -> None:
|
||||||
|
"""Create a new selection setting."""
|
||||||
|
super().__init__(name, description, default)
|
||||||
|
# Check if default is in options
|
||||||
|
if default not in options:
|
||||||
|
raise ValueError("Default value must be one of the options")
|
||||||
|
|
||||||
|
self.options = options
|
||||||
|
|
||||||
|
|
||||||
|
@property
|
||||||
|
def value(self):
|
||||||
|
"""The value of the setting. In this case, a string."""
|
||||||
|
return self._value
|
||||||
|
|
||||||
|
@value.setter
|
||||||
|
def value(self, value):
|
||||||
|
try:
|
||||||
|
if value in self.options:
|
||||||
|
self._value = value
|
||||||
|
else:
|
||||||
|
raise ValueError("Value must be one of the options")
|
||||||
|
# This fixes a bug when creating the widget when the options are not yet set
|
||||||
|
except AttributeError:
|
||||||
|
self._value = value
|
||||||
|
self.options = [value]
|
||||||
|
self.settings_changed.emit()
|
||||||
|
|
||||||
|
|
||||||
|
class StringSetting(Setting):
|
||||||
|
"""A setting that is a string.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
name (str) : The name of the setting
|
||||||
|
default : The default value of the setting
|
||||||
|
description (str) : A description of the setting
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, name: str, default: str, description: str) -> None:
|
||||||
|
"""Create a new string setting."""
|
||||||
|
super().__init__(name, description, default)
|
||||||
|
|
||||||
|
@property
|
||||||
|
def value(self):
|
||||||
|
"""The value of the setting. In this case, a string."""
|
||||||
|
return self._value
|
||||||
|
|
||||||
|
@value.setter
|
||||||
|
def value(self, value):
|
||||||
|
try:
|
||||||
|
self._value = str(value)
|
||||||
|
except ValueError:
|
||||||
|
raise ValueError("Value must be a string")
|
38
tests/simulator.py
Normal file
38
tests/simulator.py
Normal file
|
@ -0,0 +1,38 @@
|
||||||
|
# Dummy test to communicate the structure
|
||||||
|
from quackseq.pulsesequence import PulseSequence
|
||||||
|
from quackseq.event import Event
|
||||||
|
from quackseq.functions import RectFunction
|
||||||
|
|
||||||
|
|
||||||
|
seq = PulseSequence("test")
|
||||||
|
|
||||||
|
tx = Event("tx", "10u")
|
||||||
|
|
||||||
|
# tx.set_tx_amplitude(1)
|
||||||
|
#tx.set_tx_phase(0)
|
||||||
|
#tx.set_shape(RectFunction())
|
||||||
|
|
||||||
|
#seq.add_event(tx)
|
||||||
|
|
||||||
|
#blank = Event("blank", "10u")
|
||||||
|
|
||||||
|
#seq.add_event(blank)
|
||||||
|
|
||||||
|
#rx = Event("rx", "10u")
|
||||||
|
#rx.set_rx_phase(0)
|
||||||
|
#rx.set_rx(True)
|
||||||
|
|
||||||
|
#seq.add_event(rx)
|
||||||
|
|
||||||
|
#TR = Event("TR", "1ms")
|
||||||
|
|
||||||
|
#seq.add_event(TR)
|
||||||
|
|
||||||
|
#sim = Simulator()
|
||||||
|
|
||||||
|
#sim.set_averages(100)
|
||||||
|
|
||||||
|
# Returns the data at the RX event
|
||||||
|
#result = sim.run(seq)
|
||||||
|
|
||||||
|
#result.plot()
|
Loading…
Reference in a new issue