Detector properties

Detector properties#

class pyxel.detectors.Environment(temperature=None, wavelength=None)[source]#

Bases: object

Environmental attributes of the detector.

Parameters:

temperature (float, optional) – Temperature of the detector. Unit: K
wavelength (float, WavelengthHandling, optional) – Information about multi-wavelength. Unit: nm

property temperature#: Get Temperature of the detector.

property wavelength#: Get wavelength of the detector.

property numbytes#

Recursively calculates object size in bytes using Pympler library.

Returns:: int – Size of the object in bytes.

to_dict()[source]#: Get the attributes of this instance as a dict.

classmethod from_dict(dct)[source]#: Create a new instance of Geometry from a dict.

class pyxel.detectors.Characteristics(quantum_efficiency=None, charge_to_volt_conversion=None, pre_amplification=None, full_well_capacity=None, adc_bit_resolution=None, adc_voltage_range=None)[source]#

Bases: object

Characteristic attributes of the detector.

Parameters:

quantum_efficiency (float, optional) – Quantum efficiency.
charge_to_volt_conversion (float, optional) – Sensitivity of charge readout. Unit: V/e-
pre_amplification (float, optional) – Gain of pre-amplifier. Unit: V/V
full_well_capacity (float, optional) – Full well capacity. Unit: e-
adc_voltage_range (tuple of floats, optional) – ADC voltage range. Unit: V
adc_bit_resolution (int, optional) – ADC bit resolution.

property quantum_efficiency#: Get Quantum efficiency.

property charge_to_volt_conversion#: Get charge to volt conversion parameter.

property pre_amplification#: Get voltage pre-amplification gain.

property adc_bit_resolution#: Get bit resolution of the Analog-Digital Converter.

property adc_voltage_range#: Get voltage range of the Analog-Digital Converter.

property full_well_capacity#: Get Full well capacity.

property system_gain#: Get system gain.

property numbytes#

Recursively calculates object size in bytes using Pympler library.

Returns:: int – Size of the object in bytes.

to_dict()[source]#: Get the attributes of this instance as a dict.

classmethod from_dict(dct)[source]#: Create a new instance from a dict.

class pyxel.detectors.Geometry(row, col, total_thickness=None, pixel_vert_size=None, pixel_horz_size=None, pixel_scale=None)[source]#

Bases: object

Geometrical attributes of the detector.

Parameters:

row (int) – Number of pixel rows.
col (int) – Number of pixel columns.
total_thickness (float, optional) – Thickness of detector. Unit: um
pixel_vert_size (float, optional) – Vertical dimension of pixel. Unit: um
pixel_horz_size (float, optional) – Horizontal dimension of pixel. Unit: um
pixel_scale (float, optional) – Dimension of how much of the sky is covered by one pixel. Unit: arcsec/pixel

property row#: Get Number of pixel rows.

property col#: Get Number of pixel columns.

property shape#: Return detector shape.

property total_thickness#: Get Thickness of detector.

property pixel_vert_size#: Get Vertical dimension of pixel.

property pixel_horz_size#: Get Horizontal dimension of pixel.

property pixel_scale#: Get pixel scale.

property horz_dimension#

Get total horizontal dimension of detector. Calculated automatically.

Returns:: float – horizontal dimension

property vert_dimension#

Get total vertical dimension of detector. Calculated automatically.

Returns:: float – vertical dimension

vertical_pixel_center_pos_list()[source]#: Generate horizontal position list of all pixel centers in detector imaging area.

horizontal_pixel_center_pos_list()[source]#: Generate horizontal position list of all pixel centers in detector imaging area.

property numbytes#

Recursively calculates object size in bytes using Pympler library.

Returns:: int – Size of the object in bytes.

to_dict()[source]#: Get the attributes of this instance as a dict.

classmethod from_dict(dct)[source]#: Create a new instance of Geometry from a dict.

class pyxel.detectors.ReadoutProperties(times, start_time=0.0, non_destructive=False)[source]#

Bases: object

Readout sampling detector properties related to the readout process of a detector.

These properties include sampling times, readout steps, and other simulation parameters.

Parameters:

times (Sequence[Number]) – A sequence of increasing numerical values representing the times at which the detector samples are read.
start_time (float, optional. default: 0.0) – The start time for the readout process. All value in times must be greater that start_time.
non_destructive (bool, optional. Default: False) – A boolean flag indicating whether the readout simulation is non-destructive. If set to True, the readout process will not modify the underlying data.

Examples

>>> readout_properties = ReadoutProperties(times=[1, 2, 4, 7, 10], start_time=0.0)
>>> readout_properties.times
array([ 1.,  2.,  4.,  7., 10.])
>>> readout_properties.steps
array([0., 1., 2., 3., 3.])
>>> readout_properties.num_steps
5
>>> readout_properties.absolute_time
0.0

property times#

Return the sampling times for readout process.

Examples

>>> readout_properties = ReadoutProperties(
...     times=[1, 2, 4, 7, 10], start_time=0.0
... )
>>> readout_properties.times
array([ 1.,  2.,  4.,  7., 10.])

property steps#

Return the time interval between consecutive readout samples.

Examples

>>> readout_properties = ReadoutProperties(
...     times=[1, 2, 4, 7, 10], start_time=0.0
... )
>>> readout_properties.steps
array([0., 1., 2., 3., 3.])

property num_steps#

Return the total number of readout steps.

Examples

>>> readout_properties = ReadoutProperties(
...     times=[1, 2, 4, 7, 10], start_time=0.0
... )
>>> readout_properties.num_steps
5

property start_time#

Get the start time for the readout simulation.

Examples

>>> readout_properties = ReadoutProperties(
...     times=[1, 2, 4, 7, 10], start_time=0.0
... )
>>> readout_properties.start_time
0.0

property end_time#

Return the last time in the readout sequence.

Examples

>>> readout_properties = ReadoutProperties(
...     times=[1, 2, 4, 7, 10], start_time=0.0
... )
>>> readout_properties.end_time
10.0

property non_destructive#

Check if the readout process is non-destructive.

Returns:: bool – True if the readout does not alter the underlying data. False otherwise.

Examples

>>> readout_properties = ReadoutProperties(
...     times=[1, 2, 4, 7, 10], start_time=0.0
... )
>>> readout_properties.non_destructive
False

property times_linear#

Check if the time intervals between readout samples are uniform.

Returns:: bool – True is all readout steps are equal (i.e., time intervals are linear), False otherwise.

Examples

>>> readout_properties = ReadoutProperties(
...     times=[1, 2, 4, 7, 10], start_time=0.0
... )
>>> readout_properties.times_linear
False

>>> readout_properties = ReadoutProperties(
...     times=[1, 2, 3, 4, 5], start_time=0.0
... )
>>> readout_properties.times_linear
True

property time#

Get the current time within the readout simulation.

Returns:: float – The current time during the readout process.

Examples

>>> readout_properties = ReadoutProperties(
...     times=[1, 2, 3, 4, 5], start_time=0.5
... )
>>> readout_properties.time
0.0

property absolute_time#

Get the absolute time relative to the simulation start.

Returns:: float – The absolute time, calculated as start_time + time.

Examples

>>> readout_properties = ReadoutProperties(
...     times=[1, 2, 3, 4, 5], start_time=0.5
... )
>>> readout_properties.absolute_time
0.5

property time_step#

Get the step size used for advancing in the simulation.

Returns:: float – The current time step value for advancing time.

Examples

>>> readout_properties = ReadoutProperties(
...     times=[1, 2, 3, 4, 5], start_time=0.5
... )
>>> readout_properties.time_step = 1.0

property read_out#

Get the status of the readout process.

Returns:: bool – True if the readout process is active, False otherwise.

Examples

>>> readout_properties = ReadoutProperties(
...     times=[1, 2, 3, 4, 5], start_time=0.5
... )
>>> readout_properties.read_out
True

property pipeline_count#

Get the current readout pipeline count.

This count indicates the number of times the readout process has advanced.

Returns:: int – The number of completed readout steps.

property is_first_readout#

Check if the current step is the first readout time.

Returns:: bool – True if this is the first readout time, False otherwise.

Examples

>>> readout_properties = ReadoutProperties(
...     times=[1, 2, 3, 4, 5], start_time=0.5
... )
>>> readout_properties.is_first_readout
True

property is_last_readout#

Check if the current step is the last readout time.

Returns:: bool – True if this is the last readout time, False otherwise.

Examples

>>> readout_properties = ReadoutProperties(
...     times=[1, 2, 3, 4, 5], start_time=0.5
... )
>>> readout_properties.is_last_readout
False

CCD specific#

CCD specific classes.

class pyxel.detectors.CCDGeometry(row, col, total_thickness=None, pixel_vert_size=None, pixel_horz_size=None, pixel_scale=None)[source]#

Bases: Geometry

Geometrical attributes of a CCD detector.

Parameters:

row (int) – Number of pixel rows.
col (int) – Number of pixel columns.
total_thickness (float) – Thickness of detector. Unit: um
pixel_vert_size (float) – Vertical dimension of pixel. Unit: um
pixel_horz_size (float) – Horizontal dimension of pixel. Unit: um
pixel_scale (float) – Dimension of how much of the sky is covered by one pixel. Unit: arcsec/pixel

property col#: Get Number of pixel columns.

classmethod from_dict(dct)#: Create a new instance of Geometry from a dict.

horizontal_pixel_center_pos_list()#: Generate horizontal position list of all pixel centers in detector imaging area.

property horz_dimension#

Get total horizontal dimension of detector. Calculated automatically.

Returns:: float – horizontal dimension

property numbytes#

Recursively calculates object size in bytes using Pympler library.

Returns:: int – Size of the object in bytes.

property pixel_horz_size#: Get Horizontal dimension of pixel.

property pixel_scale#: Get pixel scale.

property pixel_vert_size#: Get Vertical dimension of pixel.

property row#: Get Number of pixel rows.

property shape#: Return detector shape.

to_dict()#: Get the attributes of this instance as a dict.

property total_thickness#: Get Thickness of detector.

property vert_dimension#

Get total vertical dimension of detector. Calculated automatically.

Returns:: float – vertical dimension

vertical_pixel_center_pos_list()#: Generate horizontal position list of all pixel centers in detector imaging area.

CMOS specific#

CMOS specific classes.

class pyxel.detectors.CMOSGeometry(row, col, total_thickness=None, pixel_vert_size=None, pixel_horz_size=None, pixel_scale=None)[source]#

Bases: Geometry

Geometrical attributes of a CMOS-based detector.

Parameters:

row (int) – Number of pixel rows.
col (int) – Number of pixel columns.
total_thickness (float) – Thickness of detector. Unit: um
pixel_vert_size (float) – Vertical dimension of pixel. Unit: um
pixel_horz_size (float) – Horizontal dimension of pixel. Unit: um
pixel_scale (float) – Dimension of how much of the sky is covered by one pixel. Unit: arcsec/pixel

property col#: Get Number of pixel columns.

classmethod from_dict(dct)#: Create a new instance of Geometry from a dict.

horizontal_pixel_center_pos_list()#: Generate horizontal position list of all pixel centers in detector imaging area.

property horz_dimension#

Get total horizontal dimension of detector. Calculated automatically.

Returns:: float – horizontal dimension

property numbytes#

Recursively calculates object size in bytes using Pympler library.

Returns:: int – Size of the object in bytes.

property pixel_horz_size#: Get Horizontal dimension of pixel.

property pixel_scale#: Get pixel scale.

property pixel_vert_size#: Get Vertical dimension of pixel.

property row#: Get Number of pixel rows.

property shape#: Return detector shape.

to_dict()#: Get the attributes of this instance as a dict.

property total_thickness#: Get Thickness of detector.

property vert_dimension#

Get total vertical dimension of detector. Calculated automatically.

Returns:: float – vertical dimension

vertical_pixel_center_pos_list()#: Generate horizontal position list of all pixel centers in detector imaging area.

MKID specific#

MKID specific classes.

class pyxel.detectors.MKIDGeometry(row, col, total_thickness=None, pixel_vert_size=None, pixel_horz_size=None, pixel_scale=None)[source]#

Bases: Geometry

Geometrical attributes of a MKID-based detector.

Parameters:

row (int) – Number of pixel rows.
col (int) – Number of pixel columns.
total_thickness (float) – Thickness of detector. Unit: um
pixel_vert_size (float) – Vertical dimension of pixel. Unit: um
pixel_horz_size (float) – Horizontal dimension of pixel. Unit: um
pixel_scale (float) – Dimension of how much of the sky is covered by one pixel. Unit: arcsec/pixel

property col#: Get Number of pixel columns.

classmethod from_dict(dct)#: Create a new instance of Geometry from a dict.

horizontal_pixel_center_pos_list()#: Generate horizontal position list of all pixel centers in detector imaging area.

property horz_dimension#

Get total horizontal dimension of detector. Calculated automatically.

Returns:: float – horizontal dimension

property numbytes#

Recursively calculates object size in bytes using Pympler library.

Returns:: int – Size of the object in bytes.

property pixel_horz_size#: Get Horizontal dimension of pixel.

property pixel_scale#: Get pixel scale.

property pixel_vert_size#: Get Vertical dimension of pixel.

property row#: Get Number of pixel rows.

property shape#: Return detector shape.

to_dict()#: Get the attributes of this instance as a dict.

property total_thickness#: Get Thickness of detector.

property vert_dimension#

Get total vertical dimension of detector. Calculated automatically.

Returns:: float – vertical dimension

vertical_pixel_center_pos_list()#: Generate horizontal position list of all pixel centers in detector imaging area.

APD specific#

APD specific classes.

class pyxel.detectors.APDCharacteristics(roic_gain, quantum_efficiency=None, full_well_capacity=None, adc_bit_resolution=None, adc_voltage_range=None, avalanche_gain=None, pixel_reset_voltage=None, common_voltage=None)[source]#

Bases: object

Characteristic attributes of the APD detector.

Parameters:

roic_gain – Gain of the read-out integrated circuit. Unit: V/V
quantum_efficiency (float, optional) – Quantum efficiency.
full_well_capacity (float, optional) – Full well capacity. Unit: e-
adc_bit_resolution (int, optional) – ADC bit resolution.
adc_voltage_range (tuple of floats, optional) – ADC voltage range. Unit: V
avalanche_gain (float, optional) – APD gain. Unit: electron/electron
pixel_reset_voltage (float) – DC voltage going into the detector, not the voltage of a reset pixel. Unit: V
common_voltage (float) – Common voltage. Unit: V

property quantum_efficiency#: Get Quantum efficiency.

property avalanche_gain#: Get APD gain.

property pixel_reset_voltage#: Get pixel reset voltage.

property common_voltage#: Get common voltage.

property avalanche_bias#: Get avalanche bias.

property roic_gain#: Get roic gainn.

property node_capacitance#: Get node capacitance.

property charge_to_volt_conversion#: Get charge to voltage conversion factor.

property adc_bit_resolution#: Get bit resolution of the Analog-Digital Converter.

property adc_voltage_range#: Get voltage range of the Analog-Digital Converter.

property full_well_capacity#: Get Full well capacity.

property system_gain#: Get system gain.

property numbytes#

Recursively calculates object size in bytes using Pympler library.

Returns:: int – Size of the object in bytes.

static bias_to_node_capacitance_saphira(bias)[source]#

Pixel integrating node capacitance in F.

The below interpolates empirical published data, however note that Node C = Charge Gain / Voltage Gain So can be calculated by measuring V gain (varying PRV) and chg gain (PTC); see [2]

Parameters:: bias (float)
Returns:: output_capacitance (float)

static bias_to_gain_saphira(bias)[source]#

Calculate gain from bias.

The formula ignores the soft knee between the linear and unity gain ranges, but should be close enough. [2] (Mk13 ME1000)

Parameters:: bias (float)
Returns:: float – gain

static gain_to_bias_saphira(gain)[source]#

Calculate bias from gain.

The formula ignores the soft knee between the linear and unity gain ranges, but should be close enough. [2] (Mk13 ME1000)

Parameters:: gain (float)
Returns:: bias (float)

static detector_gain_saphira(capacitance, roic_gain)[source]#

Saphira detector gain.

Parameters:

capacitance (float)
roic_gain (float)

Returns:

float

to_dict()[source]#: Get the attributes of this instance as a dict.

classmethod from_dict(dct)[source]#: Create a new instance from a dict.

class pyxel.detectors.APDGeometry(row, col, total_thickness=None, pixel_vert_size=None, pixel_horz_size=None, pixel_scale=None)[source]#

Bases: Geometry

Geometrical attributes of a APD-based detector.

Parameters:

row (int) – Number of pixel rows.
col (int) – Number of pixel columns.
total_thickness (float) – Thickness of detector. Unit: um
pixel_vert_size (float) – Vertical dimension of pixel. Unit: um
pixel_horz_size (float) – Horizontal dimension of pixel. Unit: um

property col#: Get Number of pixel columns.

classmethod from_dict(dct)#: Create a new instance of Geometry from a dict.

horizontal_pixel_center_pos_list()#: Generate horizontal position list of all pixel centers in detector imaging area.

property horz_dimension#

Get total horizontal dimension of detector. Calculated automatically.

Returns:: float – horizontal dimension

property numbytes#

Recursively calculates object size in bytes using Pympler library.

Returns:: int – Size of the object in bytes.

property pixel_horz_size#: Get Horizontal dimension of pixel.

property pixel_scale#: Get pixel scale.

property pixel_vert_size#: Get Vertical dimension of pixel.

property row#: Get Number of pixel rows.

property shape#: Return detector shape.

to_dict()#: Get the attributes of this instance as a dict.

property total_thickness#: Get Thickness of detector.

property vert_dimension#

Get total vertical dimension of detector. Calculated automatically.

Returns:: float – vertical dimension

vertical_pixel_center_pos_list()#: Generate horizontal position list of all pixel centers in detector imaging area.

Detector properties

Contents

Detector properties#

CCD specific#

CMOS specific#

MKID specific#

APD specific#