Data structures

class pyxel.data_structure.Scene

Bases: object

Scene class defining and storing information of all multi-wavelength photons (unit: ph / (cm2 nm s)).

Multi-wavelength photon information are store in form of xarray Datasets within a hierarchical structure.

Examples

Create an empty Scene

>>> from pyxel.detectors import CCD, CCDGeometry, Characteristics, Environment
>>> detector = CCD(
...     geometry=CCDGeometry(row=5, col=5),
...     characteristics=Characteristics(),
...     environment=Environment(),
... )
>>> detector.scene
Scene<no source>

Add a source

>>> import xarray as xr
>>> source = xr.Dataset(...)
>>> source
<xarray.Dataset>
Dimensions:     (ref: 345, wavelength: 343)
Coordinates:
  * ref         (ref) int64 0 1 2 3 4 5 6 7 ... 337 338 339 340 341 342 343 344
  * wavelength  (wavelength) float64 336.0 338.0 340.0 ... 1.018e+03 1.02e+03
Data variables:
    x           (ref) float64 2.057e+05 2.058e+05 ... 2.031e+05 2.03e+05
    y           (ref) float64 8.575e+04 8.58e+04 ... 8.795e+04 8.807e+04
    weight      (ref) float64 11.49 14.13 15.22 14.56 ... 15.21 11.51 8.727
    flux        (ref, wavelength) float64 0.03769 0.04137 ... 1.813 1.896
Attributes:
    right_ascension:  56.75 deg
    declination:      24.1167 deg
    fov_radius:       0.5 deg

>>> detector.scene.add_source(source)
>>> detector.scene
Scene<1 source(s)>

Get the pointing coordinates of the first source >>> detector.scene.get_pointing_coordinates() or >>> detector.scene.get_pointing_coordinates(source_idx=0) SceneCoordinates(right_ascension=<Quantity 56.75 deg>, declination=<Quantity 24.1167 deg>, fov=<Quantity 0.5 deg>)

add_source(source)

Add a source to the current scene.

Parameters:

source (Dataset)

Raises:

• TypeError – If ‘source’ is not a Dataset object.

• ValueError – If ‘source’ has not the expected format.

Examples

>>> from pyxel.detectors import CCD
>>> detector = CCD(...)

>>> source
<xarray.Dataset>
Dimensions:     (ref: 345, wavelength: 343)
Coordinates:
  * ref         (ref) int64 0 1 2 3 4 5 6 7 ... 337 338 339 340 341 342 343 344
  * wavelength  (wavelength) float64 336.0 338.0 340.0 ... 1.018e+03 1.02e+03
Data variables:
    x           (ref) float64 1.334e+03 1.434e+03 ... -1.271e+03 -1.381e+03
    y           (ref) float64 -1.009e+03 -956.1 -797.1 ... 1.195e+03 1.309e+03
    weight      (ref) float64 11.49 14.13 15.22 14.56 ... 15.21 11.51 8.727
    flux        (ref, wavelength) float64 0.003769 0.004137 ... 0.1813 0.1896
Attributes:
    right_ascension:  56.75 deg
    declination:      24.1167 deg
    fov_radius:       0.5 deg

>>> detector.scene.add_source(source)
>>> detector.scene.data
DataTree('scene', parent=None)
└── DataTree('list')
    └── DataTree('0')
            Dimensions:     (ref: 4, wavelength: 4)
            Coordinates:
              * ref         (ref) int64 0 1 2 3
              * wavelength  (wavelength) float64 336.0 338.0 1.018e+03 1.02e+03
            Data variables:
                x           (ref) float64 64.97 11.44 -55.75 -20.66
                y           (ref) float64 89.62 -129.3 -48.16 87.87
                weight      (ref) float64 14.73 12.34 14.63 14.27
                flux        (ref, wavelength) float64 0.003769 0.004137 ... 0.1813 0.1896
            Attributes:
                right_ascension:  56.75 deg
                declination:      24.1167 deg
                fov_radius:       0.5 deg

get_pointing_coordinates(source_idx=0)

Get the SceneCoordinates from a source in the scene.

property data

Get a multi-wavelength object.

empty()

Create a new empty source.

from_scopesim(source)

Convert a ScopeSim Source object into a Scene object.

Parameters:

source (scopesim.Source) – Object to convert to a Scene object.

Raises:

• RuntimeError – If package ‘scopesim’ is not installed.

• TypeError – If input parameter ‘source’ is not a ScopeSim Source object.

Notes

More information about ScopeSim Source objects at this link: https://scopesim.readthedocs.io/en/latest/reference/scopesim.source.source.html

to_scopesim()

Convert this Scene object into a ScopeSim Source object.

Returns:

Source – A ScopeSim Source object.

Notes

More information about ScopeSim Source objects at this link: https://scopesim.readthedocs.io/en/latest/reference/scopesim.source.source.html

to_dict()

Convert an instance of Scene to a dict.

classmethod from_dict(dct)

Create a new instance of a Scene object from a dict.

to_xarray()

Convert current scene to a xarray Dataset.

Returns:

xr.Dataset

Examples

>>> ds = scene.to_xarray()
>>> ds
<xarray.Dataset>
Dimensions:     (ref: 345, wavelength: 343)
Coordinates:
  * ref         (ref) int64 0 1 2 3 4 5 6 7 ... 337 338 339 340 341 342 343 344
  * wavelength  (wavelength) float64 336.0 338.0 340.0 ... 1.018e+03 1.02e+03
Data variables:
    x           (ref) float64 2.057e+05 2.058e+05 ... 2.031e+05 2.03e+05
    y           (ref) float64 8.575e+04 8.58e+04 ... 8.795e+04 8.807e+04
    weight      (ref) float64 11.49 14.13 15.22 14.56 ... 15.21 11.51 8.727
    flux        (ref, wavelength) float64 0.03769 0.04137 ... 1.813 1.896
Attributes:
    right_ascension:  56.75 deg
    declination:      24.1167 deg
    fov_radius:       0.5 deg
>>> ds["wavelength"]
<xarray.DataArray 'wavelength' (wavelength: 343)>
array([ 336.,  338.,  340., ..., 1016., 1018., 1020.])
Coordinates:
  * wavelength  (wavelength) float64 336.0 338.0 340.0 ... 1.018e+03 1.02e+03
Attributes:
    units:    nm
>>> ds["flux"]
<xarray.DataArray 'flux' (ref: 345, wavelength: 343)>
array([[3.76907117e-02, 4.13740861e-02, ..., 3.98815404e-02, 7.96581117e-01],
       [1.15190254e-02, 1.02210366e-02, ..., 2.00486326e-02, 2.05518196e-02],
       ...,
       [1.01187592e-01, 9.57637374e-02, ..., 2.71410354e-01, 2.85997559e-01],
       [1.80093381e+00, 1.69864354e+00, ..., 1.81295134e+00, 1.89642359e+00]])
Coordinates:
  * ref         (ref) int64 0 1 2 3 4 5 6 7 ... 337 338 339 340 341 342 343 344
  * wavelength  (wavelength) float64 336.0 338.0 340.0 ... 1.018e+03 1.02e+03
Attributes:
    units:    ph / (cm2 nm s)

class pyxel.data_structure.Photon(geo)

Bases: object

Photon class designed to handle the storage of monochromatic (unit: ph or multi-wavelength photons (unit ph/nm).

Monochromatic photons are stored in a 2D Numpy array and multi-wavelength photons are stored in a 3D Xarray DataArray.

Accepted array types: np.float16, np.float32, np.float64

Examples

Create an empty Photon container

>>> import numpy as np
>>> from pyxel.detectors import CCD, CCDGeometry, Characteristics, Environment
>>> detector = CCD(
...     geometry=CCDGeometry(row=5, col=5),
...     characteristics=Characteristics(),
...     environment=Environment(),
... )
>>> detector.photon
Photon<UNINITIALIZED, shape=(5, 5)>
>>> detector.ndim
0
>>> detector.shape
()

Use monochromatic photons

>>> detector.photon.empty()
>>> detector.photon.array = np.zeros(shape=(5, 5), dtype=float)
>>> detector.photon
Photon<shape=(5, 5), dtype=float64>

>>> detector.photon.array = detector.photon.array + np.ones(shape=(5, 5))
>>> detector.photon += np.ones(shape=(5, 5))

>>> detector.photon.array
array([[2., ...., 2.],
       ...,
       [2., ..., 2.]])
>>> detector.photon.to_xarray()
<xarray.DataArray 'photon' (y: 5, x: 5)>
array([[2., ...., 2.],
       ...,
       [2., ..., 2.]])
Coordinates:
  * y        (y) int64 0 1 2 3 4
  * x        (x) int64 0 1 2 3 4
Attributes:
    units:      Ph
    long_name:  Photon

Use multi-wavelength photons

>>> detector.photon.empty()
>>> new_photon_3d = xr.DataArray(
...     np.ones(shape=(4, 5, 5), dtype=float),
...     dims=["wavelength", "y", "x"],
...     coords={"wavelength": [400.0, 420.0, 440.0, 460.0]},
... )

>>> detector.photon.array_3d = detector.photon.array_3d + new_photon_3d
>>> detector.photon += new_photon_3d

>>> detector.photon.array_3d
<xarray.DataArray 'photon' (wavelength: 4, y: 5, x: 5)>
array([[[2., ...., 2.],
        ...,
        [2., ..., 2.]]])
Coordinates:
  * wavelength  (wavelength) float 400.0 ... 460.0

TYPE_LIST = (dtype('float16'), dtype('float32'), dtype('float64'))

property shape

Return the shape of the Photon container.

property ndim

property dtype

property array

Two-dimensional numpy array storing monochromatic photon.

Only accepts an array with the right type and shape.

Raises:

ValueError – Raised if ‘array’ is not initialized.

Examples

>>> from pyxel.detectors import CCD, CCDGeometry
>>> detector = CCD(geometry=CCDGeometry(row=2, col=3))

>>> import numpy as np
>>> detector.photon.array = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])

>>> detector.photon.array
array([[1., 2., 3.],
      [4., 5., 6.]])

property array_2d

Two-dimensional numpy array storing the data.

Only accepts an array with the right type and shape.

Raises:

ValueError – Raised if ‘array’ is not initialized.

property array_3d

Three-dimensional numpy array storing the data.

Only accepts an array with the right type and shape.

Raises:

ValueError – Raised if ‘array’ is not initialized.

property numbytes

Recursively calculates object size in bytes using Pympler library.

Returns:

int – Size of the object in bytes.

to_dict()

classmethod from_dict(geometry, data)

to_xarray(dtype=None)

plot(robust=True)

Plot the array using Matplotlib.

Parameters:

robust (bool, optional) – If True, the colormap is computed with 2nd and 98th percentile instead of the extreme values.

Examples

>>> detector.photon.plot()

empty()

Empty the data container.

class pyxel.data_structure.Charge(geo)

Bases: object

Charge class representing charge distribution (unit: e⁻).

This class manipulates charge data in the form of a Numpy array and Pandas dataframe.

EXP_TYPE

alias of float

TYPE_LIST = (dtype('float16'), dtype('float32'), dtype('float64'))

static create_charges(*, particle_type, particles_per_cluster, init_energy, init_ver_position, init_hor_position, init_z_position, init_ver_velocity, init_hor_velocity, init_z_velocity)

Create new charge(s) or group of charge(s) as a DataFrame.

Parameters:

• particle_type (str) – Type of particle. Valid values: ‘e’ for an electron or ‘h’ for a hole.

• particles_per_cluster (array-like)

• init_energy (array)

• init_ver_position (array)

• init_hor_position (array)

• init_z_position (array)

• init_ver_velocity (array)

• init_hor_velocity (array)

• init_z_velocity (array)

Returns:

DataFrame – Charge(s) stored in a DataFrame.

convert_df_to_array()

Convert charge dataframe to an array.

Charge in the detector volume is collected and assigned to the nearest pixel.

static convert_array_to_df(array, num_rows, num_cols, pixel_vertical_size, pixel_horizontal_size)

Convert charge array to a dataframe placing charge packets in pixels to the center and on top of pixels.

Parameters:

• array (ndarray)

• num_rows (int)

• num_cols (int)

• pixel_vertical_size (float)

• pixel_horizontal_size (float)

Returns:

DataFrame

add_charge_dataframe(new_charges)

Add new charge(s) or group of charge(s) to the charge dataframe.

Parameters:

new_charges (DataFrame) – Charges as a DataFrame

add_charge(*, particle_type, particles_per_cluster, init_energy, init_ver_position, init_hor_position, init_z_position, init_ver_velocity, init_hor_velocity, init_z_velocity)

Add new charge(s) or group of charge(s) inside the detector.

Parameters:

• particle_type (str) – Type of particle. Valid values: ‘e’ for an electron or ‘h’ for a hole.

• particles_per_cluster (array)

• init_energy (array)

• init_ver_position (array)

• init_hor_position (array)

• init_z_position (array)

• init_ver_velocity (array)

• init_hor_velocity (array)

• init_z_velocity (array)

add_charge_array(array)

Add charge to the charge array. Add to charge dataframe if not empty instead.

Parameters:

array (ndarray)

property array

Get charge in a numpy array.

to_xarray()

Convert into a DataArray object.

property frame

Get charge in a pandas dataframe.

empty()

Empty all data stored in Charge class.

frame_empty()

Return True if frame is empty and False otherwise.

validate_type(value)

Validate a value.

Parameters:

value

validate_shape(value)

TBW.

get_frame_values(quantity, id_list=None)

Get quantity values of particles defined with id_list. By default it returns values of all particles.

Parameters:

• quantity (str) – Name of the quantity: number, energy, position_ver, velocity_hor, etc.

• id_list (sequence of int) – List of particle ids: [0, 12, 321]

Returns:

array

set_frame_values(quantity, new_value_list, id_list=None)

Update quantity values of particles defined with id_list. By default it updates all.

Parameters:

• quantity (str) – Name of the quantity: number, energy, position_ver, velocity_hor, etc.

• new_value_list (sequence of int) – List of values [1.12, 2.23, 3.65]

• id_list (sequence of int) – List of particle ids: [0, 12, 321]

remove_from_frame(id_list=None)

Remove particles defined with id_list. By default it removes all particles from DataFrame.

Parameters:

id_list (sequence of int) – List of particle ids: [0, 12, 321]

class pyxel.data_structure.Pixel(geo)

Bases: ArrayBase

Pixel class defining and storing information of charge packets within pixel (unit: e⁻).

Accepted array types: np.int32, np.int64, np.uint32, np.uint64, np.float16, np.float32, np.float64.

TYPE_LIST = (dtype('float16'), dtype('float32'), dtype('float64'))

NAME = 'Pixel'

UNIT = 'e⁻'

empty()

Empty the array by setting the array to zero array in detector shape.

update(data)

Update ‘array’ attribute.

This method updates ‘array’ attribute of this object with new data. If the data is None, then the object is empty.

Parameters:

data (array_like, Optional)

Examples

>>> from pyxel.data_structure import Photon
>>> obj = Photon(...)
>>> obj.update([[1, 2], [3, 4]])
>>> obj.array
array([[1, 2], [3, 4]])

>>> obj.update(None)  # Equivalent to obj.empty()

property array

Two-dimensional numpy array storing the data.

Only accepts an array with the right type and shape.

Raises:

ValueError – Raised if ‘array’ is not initialized.

property dtype

Return array data type.

property ndim

Return number of dimensions of the array.

property numbytes

Recursively calculates object size in bytes using Pympler library.

Returns:

int – Size of the object in bytes.

plot(robust=True)

Plot the array using Matplotlib.

Parameters:

robust (bool, optional) – If True, the colormap is computed with 2nd and 98th percentile instead of the extreme values.

Examples

>>> detector.photon.plot()

property shape

Return array shape.

to_xarray(dtype=None)

Convert into a 2D DataArray object with dimensions ‘y’ and ‘x’.

Parameters:

dtype (data-type, optional) – Force a data-type for the array.

Returns:

DataArray – 2D DataArray objects.

Examples

>>> detector.photon.to_xarray(dtype=float)
<xarray.DataArray 'photon' (y: 100, x: 100)>
array([[15149., 15921., 15446., ..., 15446., 15446., 16634.],
       [15149., 15446., 15446., ..., 15921., 16396., 17821.],
       [14555., 14971., 15446., ..., 16099., 16337., 17168.],
       ...,
       [16394., 16334., 16334., ..., 16562., 16325., 16325.],
       [16334., 15978., 16215., ..., 16444., 16444., 16206.],
       [16097., 15978., 16215., ..., 16681., 16206., 16206.]])
Coordinates:
  * y        (y) int64 0 1 2 3 4 5 6 7 8 9 10 ... 90 91 92 93 94 95 96 97 98 99
  * x        (x) int64 0 1 2 3 4 5 6 7 8 9 10 ... 90 91 92 93 94 95 96 97 98 99
Attributes:
    units:      Ph

class pyxel.data_structure.Phase(geo)

Bases: ArrayBase

Phase class.

Accepted array types: np.float16, np.float32 and np.float64.

TYPE_LIST = (dtype('float16'), dtype('float32'), dtype('float64'))

NAME = 'Phase'

UNIT = ''

property array

Two-dimensional numpy array storing the data.

Only accepts an array with the right type and shape.

Raises:

ValueError – Raised if ‘array’ is not initialized.

property dtype

Return array data type.

empty()

Empty the array by setting the array to None.

property ndim

Return number of dimensions of the array.

property numbytes

Recursively calculates object size in bytes using Pympler library.

Returns:

int – Size of the object in bytes.

plot(robust=True)

Plot the array using Matplotlib.

Parameters:

robust (bool, optional) – If True, the colormap is computed with 2nd and 98th percentile instead of the extreme values.

Examples

>>> detector.photon.plot()

property shape

Return array shape.

to_xarray(dtype=None)

Convert into a 2D DataArray object with dimensions ‘y’ and ‘x’.

Parameters:

dtype (data-type, optional) – Force a data-type for the array.

Returns:

DataArray – 2D DataArray objects.

Examples

>>> detector.photon.to_xarray(dtype=float)
<xarray.DataArray 'photon' (y: 100, x: 100)>
array([[15149., 15921., 15446., ..., 15446., 15446., 16634.],
       [15149., 15446., 15446., ..., 15921., 16396., 17821.],
       [14555., 14971., 15446., ..., 16099., 16337., 17168.],
       ...,
       [16394., 16334., 16334., ..., 16562., 16325., 16325.],
       [16334., 15978., 16215., ..., 16444., 16444., 16206.],
       [16097., 15978., 16215., ..., 16681., 16206., 16206.]])
Coordinates:
  * y        (y) int64 0 1 2 3 4 5 6 7 8 9 10 ... 90 91 92 93 94 95 96 97 98 99
  * x        (x) int64 0 1 2 3 4 5 6 7 8 9 10 ... 90 91 92 93 94 95 96 97 98 99
Attributes:
    units:      Ph

update(data)

Update ‘array’ attribute.

This method updates ‘array’ attribute of this object with new data. If the data is None, then the object is empty.

Parameters:

data (array_like, Optional)

Examples

>>> from pyxel.data_structure import Photon
>>> obj = Photon(...)
>>> obj.update([[1, 2], [3, 4]])
>>> obj.array
array([[1, 2], [3, 4]])

>>> obj.update(None)  # Equivalent to obj.empty()

class pyxel.data_structure.Signal(geo)

Bases: ArrayBase

Signal class defining and storing information of detector signal (unit: Volt).

Accepted array types: np.float16, np.float32, np.float64.

TYPE_LIST = (dtype('float16'), dtype('float32'), dtype('float64'))

NAME = 'Signal'

UNIT = 'Volt'

property array

Two-dimensional numpy array storing the data.

Only accepts an array with the right type and shape.

Raises:

ValueError – Raised if ‘array’ is not initialized.

property dtype

Return array data type.

empty()

Empty the array by setting the array to None.

property ndim

Return number of dimensions of the array.

property numbytes

Recursively calculates object size in bytes using Pympler library.

Returns:

int – Size of the object in bytes.

plot(robust=True)

Plot the array using Matplotlib.

Parameters:

robust (bool, optional) – If True, the colormap is computed with 2nd and 98th percentile instead of the extreme values.

Examples

>>> detector.photon.plot()

property shape

Return array shape.

to_xarray(dtype=None)

Convert into a 2D DataArray object with dimensions ‘y’ and ‘x’.

Parameters:

dtype (data-type, optional) – Force a data-type for the array.

Returns:

DataArray – 2D DataArray objects.

Examples

>>> detector.photon.to_xarray(dtype=float)
<xarray.DataArray 'photon' (y: 100, x: 100)>
array([[15149., 15921., 15446., ..., 15446., 15446., 16634.],
       [15149., 15446., 15446., ..., 15921., 16396., 17821.],
       [14555., 14971., 15446., ..., 16099., 16337., 17168.],
       ...,
       [16394., 16334., 16334., ..., 16562., 16325., 16325.],
       [16334., 15978., 16215., ..., 16444., 16444., 16206.],
       [16097., 15978., 16215., ..., 16681., 16206., 16206.]])
Coordinates:
  * y        (y) int64 0 1 2 3 4 5 6 7 8 9 10 ... 90 91 92 93 94 95 96 97 98 99
  * x        (x) int64 0 1 2 3 4 5 6 7 8 9 10 ... 90 91 92 93 94 95 96 97 98 99
Attributes:
    units:      Ph

update(data)

Update ‘array’ attribute.

This method updates ‘array’ attribute of this object with new data. If the data is None, then the object is empty.

Parameters:

data (array_like, Optional)

Examples

>>> from pyxel.data_structure import Photon
>>> obj = Photon(...)
>>> obj.update([[1, 2], [3, 4]])
>>> obj.array
array([[1, 2], [3, 4]])

>>> obj.update(None)  # Equivalent to obj.empty()

class pyxel.data_structure.Image(geo)

Bases: ArrayBase

Image class defining and storing information of detector image (unit: adu).

Accepted array types: np.uint16, np.uint32, np.uint64

TYPE_LIST = (dtype('uint8'), dtype('uint16'), dtype('uint32'), dtype('uint64'))

NAME = 'Image'

UNIT = 'adu'

property array

Two-dimensional numpy array storing the data.

Only accepts an array with the right type and shape.

Raises:

ValueError – Raised if ‘array’ is not initialized.

property dtype

Return array data type.

empty()

Empty the array by setting the array to None.

property ndim

Return number of dimensions of the array.

property numbytes

Recursively calculates object size in bytes using Pympler library.

Returns:

int – Size of the object in bytes.

plot(robust=True)

Plot the array using Matplotlib.

Parameters:

robust (bool, optional) – If True, the colormap is computed with 2nd and 98th percentile instead of the extreme values.

Examples

>>> detector.photon.plot()

property shape

Return array shape.

to_xarray(dtype=None)

Convert into a 2D DataArray object with dimensions ‘y’ and ‘x’.

Parameters:

dtype (data-type, optional) – Force a data-type for the array.

Returns:

DataArray – 2D DataArray objects.

Examples

>>> detector.photon.to_xarray(dtype=float)
<xarray.DataArray 'photon' (y: 100, x: 100)>
array([[15149., 15921., 15446., ..., 15446., 15446., 16634.],
       [15149., 15446., 15446., ..., 15921., 16396., 17821.],
       [14555., 14971., 15446., ..., 16099., 16337., 17168.],
       ...,
       [16394., 16334., 16334., ..., 16562., 16325., 16325.],
       [16334., 15978., 16215., ..., 16444., 16444., 16206.],
       [16097., 15978., 16215., ..., 16681., 16206., 16206.]])
Coordinates:
  * y        (y) int64 0 1 2 3 4 5 6 7 8 9 10 ... 90 91 92 93 94 95 96 97 98 99
  * x        (x) int64 0 1 2 3 4 5 6 7 8 9 10 ... 90 91 92 93 94 95 96 97 98 99
Attributes:
    units:      Ph

update(data)

Update ‘array’ attribute.

This method updates ‘array’ attribute of this object with new data. If the data is None, then the object is empty.

Parameters:

data (array_like, Optional)

Examples

>>> from pyxel.data_structure import Photon
>>> obj = Photon(...)
>>> obj.update([[1, 2], [3, 4]])
>>> obj.array
array([[1, 2], [3, 4]])

>>> obj.update(None)  # Equivalent to obj.empty()

class pyxel.data_structure.ArrayBase(shape)

Bases: object

Base Array class.

TYPE_LIST = ()

NAME = ''

UNIT = ''

property shape

Return array shape.

property ndim

Return number of dimensions of the array.

property dtype

Return array data type.

empty()

Empty the array by setting the array to None.

property array

Two-dimensional numpy array storing the data.

Only accepts an array with the right type and shape.

Raises:

ValueError – Raised if ‘array’ is not initialized.

update(data)

Update ‘array’ attribute.

This method updates ‘array’ attribute of this object with new data. If the data is None, then the object is empty.

Parameters:

data (array_like, Optional)

Examples

>>> from pyxel.data_structure import Photon
>>> obj = Photon(...)
>>> obj.update([[1, 2], [3, 4]])
>>> obj.array
array([[1, 2], [3, 4]])

>>> obj.update(None)  # Equivalent to obj.empty()

property numbytes

Recursively calculates object size in bytes using Pympler library.

Returns:

int – Size of the object in bytes.

to_xarray(dtype=None)

Convert into a 2D DataArray object with dimensions ‘y’ and ‘x’.

Parameters:

dtype (data-type, optional) – Force a data-type for the array.

Returns:

DataArray – 2D DataArray objects.

Examples

>>> detector.photon.to_xarray(dtype=float)
<xarray.DataArray 'photon' (y: 100, x: 100)>
array([[15149., 15921., 15446., ..., 15446., 15446., 16634.],
       [15149., 15446., 15446., ..., 15921., 16396., 17821.],
       [14555., 14971., 15446., ..., 16099., 16337., 17168.],
       ...,
       [16394., 16334., 16334., ..., 16562., 16325., 16325.],
       [16334., 15978., 16215., ..., 16444., 16444., 16206.],
       [16097., 15978., 16215., ..., 16681., 16206., 16206.]])
Coordinates:
  * y        (y) int64 0 1 2 3 4 5 6 7 8 9 10 ... 90 91 92 93 94 95 96 97 98 99
  * x        (x) int64 0 1 2 3 4 5 6 7 8 9 10 ... 90 91 92 93 94 95 96 97 98 99
Attributes:
    units:      Ph

plot(robust=True)

Plot the array using Matplotlib.

Parameters:

robust (bool, optional) – If True, the colormap is computed with 2nd and 98th percentile instead of the extreme values.

Examples

>>> detector.photon.plot()