import pathlib
import numpy as np
import astropy.units as u
import astropy.time
import named_arrays as na
__all__ = [
"efficiency_vs_wavelength",
"efficiency_vs_x",
"efficiency_vs_y",
"efficiency_vs_angle_0deg",
"efficiency_vs_angle_3deg",
]
_directory_data = pathlib.Path(__file__).parent / "_data"
serial_number = "UBO-16-017"
time_measurement = astropy.time.Time("2018-01-21")
[docs]
def efficiency_vs_wavelength() -> (
na.FunctionArray[na.TemporalSpectralDirectionalVectorArray, na.ScalarArray]
):
"""
Load the measured efficiency as a function of wavelength.
This includes contributions from both the coatings and grooves and was
measured by Eric Gullikson.
Examples
--------
Plot the efficiency vs wavelength measurements using matplotlib.
.. jupyter-execute::
import matplotlib.pyplot as plt
import astropy.visualization
import named_arrays as na
from esis.flights.f1.optics import gratings
# Load the efficiency measurements
efficiency = gratings.efficiencies.efficiency_vs_wavelength()
# Plot the measurements using matplotlib
with astropy.visualization.quantity_support():
fig, ax = plt.subplots()
na.plt.plot(
efficiency.inputs.wavelength,
efficiency.outputs,
ax=ax,
label=efficiency.inputs.time.strftime("%Y-%m-%d"),
);
ax.set_xlabel(f"wavelength ({ax.get_xlabel()})");
ax.set_ylabel(f"efficiency");
ax.legend();
"""
wavelength, efficiency = np.loadtxt(
fname=_directory_data / "mul063315.abs",
unpack=True,
skiprows=1,
)
wavelength = (wavelength * u.nm).to(u.AA)
wavelength = na.ScalarArray(wavelength, axes="wavelength")
efficiency = na.ScalarArray(efficiency, axes="wavelength")
return na.FunctionArray(
inputs=na.TemporalSpectralDirectionalVectorArray(
time=time_measurement,
wavelength=wavelength,
direction=0 * u.deg,
),
outputs=efficiency,
)
[docs]
def efficiency_vs_x() -> (
na.FunctionArray[na.TemporalSpectralPositionalVectorArray, na.ScalarArray]
):
"""
Load the measured efficiency as a function of position, :math:`x`.
This includes contributions from both the coatings and grooves and was
measured by Eric Gullikson.
Examples
--------
Plot the efficiency vs :math:`x` position measurements using matplotlib.
.. jupyter-execute::
import matplotlib.pyplot as plt
import astropy.visualization
import named_arrays as na
from esis.flights.f1.optics import gratings
# Load the efficiency measurements
efficiency = gratings.efficiencies.efficiency_vs_x()
# Plot the measurements using matplotlib
with astropy.visualization.quantity_support():
fig, ax = plt.subplots()
na.plt.plot(
efficiency.inputs.position,
efficiency.outputs,
ax=ax,
label=efficiency.inputs.time.strftime("%Y-%m-%d"),
);
ax.set_xlabel(f"$x$ ({ax.get_xlabel()})");
ax.set_ylabel(f"efficiency");
ax.legend();
"""
x, efficiency = np.loadtxt(
fname=_directory_data / "mul063283.abs",
unpack=True,
skiprows=1,
)
x = x << u.mm
x = na.ScalarArray(x, axes="grating_x")
efficiency = na.ScalarArray(efficiency, axes="grating_x")
return na.FunctionArray(
inputs=na.TemporalSpectralPositionalVectorArray(
time=time_measurement,
wavelength=63 * u.nm,
position=x,
),
outputs=efficiency,
)
[docs]
def efficiency_vs_y() -> (
na.FunctionArray[na.TemporalSpectralPositionalVectorArray, na.ScalarArray]
):
"""
Load the measured efficiency as a function of position, :math:`y`.
This includes contributions from both the coatings and grooves and was
measured by Eric Gullikson.
Examples
--------
Plot the efficiency vs :math:`y` position measurements using matplotlib.
.. jupyter-execute::
import matplotlib.pyplot as plt
import astropy.visualization
import named_arrays as na
from esis.flights.f1.optics import gratings
# Load the efficiency measurements
efficiency = gratings.efficiencies.efficiency_vs_y()
# Plot the measurements using matplotlib
with astropy.visualization.quantity_support():
fig, ax = plt.subplots()
na.plt.plot(
efficiency.inputs.position,
efficiency.outputs,
ax=ax,
label=efficiency.inputs.time.strftime("%Y-%m-%d"),
);
ax.set_xlabel(f"$y$ ({ax.get_xlabel()})");
ax.set_ylabel(f"efficiency");
ax.legend();
"""
y, efficiency = np.loadtxt(
fname=_directory_data / "mul063282.txt",
unpack=True,
skiprows=1,
)
y = y << u.mm
y = na.ScalarArray(y, axes="grating_y")
efficiency = na.ScalarArray(efficiency, axes="grating_y")
return na.FunctionArray(
inputs=na.TemporalSpectralPositionalVectorArray(
time=time_measurement,
wavelength=63 * u.nm,
position=y,
),
outputs=efficiency,
)
[docs]
def efficiency_vs_angle_0deg() -> (
na.FunctionArray[na.TemporalSpectralDirectionalVectorArray, na.ScalarArray]
):
"""
Load the measured efficiency at normal incidence as a function of output angle.
This includes contributions from both the coatings and grooves and was
measured by Eric Gullikson.
Examples
--------
Plot the efficiency vs output angle measurements using matplotlib.
.. jupyter-execute::
import matplotlib.pyplot as plt
import astropy.visualization
import named_arrays as na
from esis.flights.f1.optics import gratings
# Load the efficiency measurements
efficiency = gratings.efficiencies.efficiency_vs_angle_0deg()
# Plot the measurements using matplotlib
with astropy.visualization.quantity_support():
fig, ax = plt.subplots()
na.plt.plot(
efficiency.inputs.direction.output,
efficiency.outputs,
ax=ax,
label=efficiency.inputs.direction.input,
);
ax.set_xlabel(f"output angle ({ax.get_xlabel()})");
ax.set_ylabel(f"efficiency");
ax.legend();
"""
angle, efficiency = np.loadtxt(
fname=_directory_data / "mul063284.txt",
unpack=True,
skiprows=1,
)
angle = angle << u.deg
angle = na.ScalarArray(angle, axes="grating_output_angle")
efficiency = na.ScalarArray(efficiency, axes="grating_output_angle")
return na.FunctionArray(
inputs=na.TemporalSpectralDirectionalVectorArray(
time=time_measurement,
wavelength=63 * u.nm,
direction=na.InputOutputVectorArray(
input=0 * u.deg,
output=angle,
),
),
outputs=efficiency,
)
[docs]
def efficiency_vs_angle_3deg() -> (
na.FunctionArray[na.TemporalSpectralDirectionalVectorArray, na.ScalarArray]
):
"""
Load the measured efficiency at oblique incidence as a function of output angle.
Measured at an input angle of 3 degrees.
This includes contributions from both the coatings and grooves and was
measured by Eric Gullikson.
Examples
--------
Plot the efficiency vs output angle measurements using matplotlib.
.. jupyter-execute::
import matplotlib.pyplot as plt
import astropy.visualization
import named_arrays as na
from esis.flights.f1.optics import gratings
# Load the efficiency measurements
efficiency = gratings.efficiencies.efficiency_vs_angle_3deg()
# Plot the measurements using matplotlib
with astropy.visualization.quantity_support():
fig, ax = plt.subplots()
na.plt.plot(
efficiency.inputs.direction.output,
efficiency.outputs,
ax=ax,
label=efficiency.inputs.direction.input,
);
ax.set_xlabel(f"output angle ({ax.get_xlabel()})");
ax.set_ylabel(f"efficiency");
ax.legend();
"""
angle, efficiency = np.loadtxt(
fname=_directory_data / "mul063281.txt",
unpack=True,
skiprows=1,
)
angle = angle << u.deg
angle = na.ScalarArray(angle, axes="grating_output_angle")
efficiency = na.ScalarArray(efficiency, axes="grating_output_angle")
return na.FunctionArray(
inputs=na.TemporalSpectralDirectionalVectorArray(
time=time_measurement,
wavelength=63 * u.nm,
direction=na.InputOutputVectorArray(
input=3 * u.deg,
output=angle,
),
),
outputs=efficiency,
)
