def dataframe(self) -> pandas.DataFrame:
dataframe = super().dataframe
dataframe['half width'] = [
format.quantity(self.half_width_x.to(u.arcmin))
]
dataframe['half height'] = [
format.quantity(self.half_width_y.to(u.arcmin))
]
return dataframe
def dataframe(self) -> pandas.DataFrame:
dataframe = super().dataframe
dataframe['obscured half-width'] = [
format.quantity(self.obscured_half_width.to(u.mm))
]
# dataframe['number of sides'] = [self.num_sides]
return dataframe
def dataframe(self) -> pandas.DataFrame:
dataframe = super().dataframe
for coeff, name in zip(self.coefficients, self.coefficient_names):
coeff = coeff[..., 0, 0, 0]
if (np.abs(coeff.value) > 0.1).any():
sci_notation = False
else:
sci_notation = True
if np.isscalar(coeff):
dataframe[name] = [
fmt.quantity(coeff, scientific_notation=sci_notation)
]
else:
dataframe[name] = [
fmt.quantity(c, scientific_notation=sci_notation)
for c in coeff.T.flatten()
]
if self.output_name is not None:
dataframe.index = [self.output_name]
return dataframe
def set_variable_quantity(
self,
name: str,
value: u.Quantity,
scientific_notation: typ.Optional[bool] = None,
digits_after_decimal: int = 3,
) -> typ.NoReturn:
self.set_variable(
name=name,
value=pylatex.NoEscape(format.quantity(
a=value,
scientific_notation=scientific_notation,
digits_after_decimal=digits_after_decimal,
))
)
def dataframe(self) -> pandas.DataFrame:
dataframe = super().dataframe
dataframe['inclination'] = [
format.quantity(self.inclination.to(u.deg))
]
dataframe['clocking'] = [format.quantity(self.clocking.to(u.deg))]
dataframe['clear radius'] = [
format.quantity(self.clear_radius.to(u.mm))
]
dataframe['border width'] = [
format.quantity(self.border_width.to(u.mm))
]
dataframe['thickness'] = [format.quantity(self.thickness.to(u.nm))]
dataframe['oxide thickness'] = [
format.quantity(self.thickness_oxide.to(u.nm))
]
dataframe['mesh ratio'] = [
format.quantity(self.mesh_ratio.to(u.percent))
]
dataframe['mesh pitch'] = [format.quantity(self.mesh_pitch)]
return dataframe
def dataframe(self) -> pandas.DataFrame:
dataframe = super().dataframe
dataframe['inclination'] = [
format.quantity(self.inclination.to(u.deg))
]
dataframe['tangential radius'] = [
format.quantity(self.tangential_radius.to(u.mm))
]
dataframe['sagittal radius'] = [
format.quantity(self.sagittal_radius.to(u.mm))
]
dataframe['slope error'] = [format.quantity(self.slope_error.value)]
dataframe['ripple'] = [format.quantity(self.ripple.value)]
dataframe['microroughness'] = [
format.quantity(self.microroughness.value)
]
dataframe['nominal alpha'] = [
format.quantity(self.nominal_input_angle.to(u.deg))
]
dataframe['nominal beta'] = [
format.quantity(self.nominal_output_angle.to(u.deg))
]
dataframe['diffraction order'] = [
format.quantity(self.diffraction_order)
]
dataframe['nominal ruling density'] = [
format.quantity(self.ruling_density.to(1 / u.mm))
]
dataframe['linear ruling coefficient'] = [
format.quantity(self.ruling_spacing_coeff_linear,
scientific_notation=True)
]
dataframe['quadratic ruling coefficient'] = [
format.quantity(self.ruling_spacing_coeff_quadratic,
scientific_notation=True)
]
dataframe['cubic ruling coefficient'] = [
format.quantity(self.ruling_spacing_coeff_cubic,
scientific_notation=True)
]
dataframe['aperture wedge angle'] = [
format.quantity(self.aper_wedge_angle.to(u.deg))
]
dataframe['inner half-width'] = [
format.quantity(self.inner_half_width.to(u.mm))
]
dataframe['outer half-width'] = [
format.quantity(self.outer_half_width.to(u.mm))
]
dataframe['border width'] = [
format.quantity(self.border_width.to(u.mm))
]
dataframe['inner border width'] = [
format.quantity(self.inner_border_width.to(u.mm))
]
dataframe['dynamic clearance'] = [
format.quantity(self.dynamic_clearance.to(u.mm))
]
dataframe['substrate thickness'] = [
format.quantity(self.substrate_thickness.to(u.mm))
]
return dataframe
def dataframe(self) -> pandas.DataFrame:
dataframe = super().dataframe
dataframe['clear radius'] = [format.quantity(self.clear_radius.to(u.mm))]
return dataframe
def dataframe(self) -> pandas.DataFrame:
dataframe = super().dataframe
dataframe['clear radius'] = [format.quantity(self.clear_radius.to(u.mm))]
dataframe['mechanical radius'] = [format.quantity(self.mech_radius.to(u.mm))]
dataframe['number of sides'] = [self.num_sides]
return dataframe
def dataframe(self) -> pandas.DataFrame:
dataframe = super().dataframe
dataframe['manufacturer'] = [self.manufacturer]
dataframe['focus adjustment range'] = [
format.quantity(self.range_focus_adjustment)
]
dataframe['inclination'] = [
format.quantity(self.inclination.to(u.deg))
]
dataframe['pixel width'] = [format.quantity(self.pixel_width.to(u.um))]
dataframe['pixel array shape'] = [self.num_pixels]
dataframe['right border width'] = [
format.quantity(self.border_width_right.to(u.mm))
]
dataframe['left border width'] = [
format.quantity(self.border_width_left.to(u.mm))
]
dataframe['top border width'] = [
format.quantity(self.border_width_top.to(u.mm))
]
dataframe['bottom border width'] = [
format.quantity(self.border_width_bottom.to(u.mm))
]
dataframe['dynamic clearance'] = [
format.quantity(self.dynamic_clearance.to(u.mm))
]
dataframe['overscan pixels'] = [self.npix_overscan]
dataframe['blank pixels'] = [self.npix_blank]
dataframe['temperature'] = [format.quantity(self.temperature)]
dataframe['gain'] = [format.quantity(self.gain)]
dataframe['readout noise'] = [format.quantity(self.readout_noise)]
dataframe['dark current'] = [format.quantity(self.dark_current)]
dataframe['charge diffusion'] = [
format.quantity(self.charge_diffusion)
]
dataframe['frame transfer time'] = [
format.quantity(self.time_frame_transfer)
]
dataframe['readout time'] = [format.quantity(self.time_readout)]
dataframe['exposure length'] = [format.quantity(self.exposure_length)]
dataframe['minimum exposure length'] = [
format.quantity(self.exposure_length_min)
]
dataframe['maximum exposure length'] = [
format.quantity(self.exposure_length_max)
]
dataframe['exposure length increment'] = [
format.quantity(self.exposure_length_increment)
]
dataframe['analog-to-digital bits'] = [self.bits_analog_to_digital]
dataframe['trigger index'] = [self.index_trigger]
dataframe['synchronization error'] = [
format.quantity(self.error_synchronization)
]
return dataframe