ImageHeader

class msfc_ccd.ImageHeader(pixel, time_start, timedelta, timedelta_requested, serial_number=None, run_mode=None, status=None, voltage_fpga_vccint=0, voltage_fpga_vccaux=0, voltage_fpga_vccbram=0, temperature_fpga=0, temperature_adc_1=0, temperature_adc_2=0, temperature_adc_3=0, temperature_adc_4=0)

Bases: AbstractExplicitCartesianVectorArray

A singe FITS header or a sequence of FITS headers saved by MSFC camera.

Attributes

axes	A tuple of str representing the names of each dimension of this array.
axes_flattened	Combine axes into a single str.
broadcasted	if this array has multiple components, broadcast them against each other.
cartesian_nd	Convert any instance of AbstractVectorArray to an instance of AbstractCartesianNdVectorArray
components	The vector components of this array expressed as a dict where the keys are the names of the component.
entries	The scalar entries that compose this object.
explicit	Converts this array to an instance of named_arrays.AbstractExplicitArray.
indices	Compute the index of each element of this array.
length	L2-norm of this array.
matrix	Cast this vector into its matrix representation.
ndim	Number of dimensions of the array.
normalized	Return a normalized copy of this vector, where length is unity.
pixel	The indices of each pixel in the image.
prototype_vector	Return vector of same type with all components zeroed.
run_mode	The Run Mode of the camera when each image was captured.
serial_number	The serial number of the camera that captured each image.
shape	The number of elements along each axis of the array.
size	Total number of elements in the array.
status	The status of the camera while each image was being captured.
temperature_adc_1	Temperature 1 of the ADC when each image was captured.
temperature_adc_2	Temperature 2 of the ADC when each image was captured.
temperature_adc_3	Temperature 3 of the ADC when each image was captured.
temperature_adc_4	Temperature 4 of the ADC when each image was captured.
temperature_fpga	The temperature of the FPGA when each image was captured.
time	The time in UTC at the midpoint of the exposure.
time_end	The time in UTC at the end of the exposure.
time_start	The time in UTC at the start of the exposure.
timedelta	The measured exposure time of each image.
timedelta_requested	The requested exposure time of each image.
type_abstract	The named_arrays.AbstractArray type corresponding to this array.
type_explicit	The named_arrays.AbstractExplicitArray type corresponding to this array.
type_matrix	The corresponding named_arrays.AbstractMatrixArray class
value	Returns a new array with its units removed, if they exist.
voltage_fpga_vccaux	The VCCAUX voltage of the FPGA when each image was captured.
voltage_fpga_vccbram	The VCCBRAM voltage of the FPGA when each image was captured.
voltage_fpga_vccint	The VCCINT voltage of the FPGA when each image was captured.

Methods

__init__(pixel, time_start, timedelta, ...)
add_axes(axes)	Add new singleton axes to this array.
all([axis, where])	Return True if all of the elements along the given axes are True.
any([axis, where])	Return True if any of the elements along the given axes are True.
astype(dtype[, order, casting, subok, copy])	Copy of the array cast to a specific data type.
broadcast_to(shape[, append])	A new view of this array with the specified shape.
cell_centers([axis, random])	Convert an array from cell vertices to cell centers.
combine_axes([axes, axis_new])	Combine some of the axes of the array into a single new axis.
copy()	Create a deep copy of this array.
copy_shallow()	Create a shallow copy of this array.
from_cartesian_nd(array[, like])	Construct a new instance of this class using an instance of named_arrays.CartesianNdVectorArray.
from_components(components)	Construct a new instance of this class using a dict of components.
from_scalar(scalar[, like])	Convert a scalar (an instance of named_arrays.AbstractScalar) into a vector.
from_scalar_array(a[, like])	Constructs a new version of this array using a as the underlying data.
index(value[, axis])
index_secant(value[, axis])
interp_linear(item)	Linearly interpolate this array to find its value at the given fractional index.
max([axis, initial, where])	The maximum value of this array along the given axes.
mean([axis, where])	The mean value of this array along the given axes.
median([axis])	The median value of this array along the given axes.
min([axis, initial, where])	The minimum value of this array along the given axes.
ndindex([axis_ignored])	An iterator that yields the index of each element of this array.
percentile(q[, axis, out, overwrite_input, ...])	The requested percentile of this array along the given axes.
ptp([axis])	The peak-to-peak value of this array along the given axes.
replace(**changes)	A method version of dataclasses.replace() for named arrays.
reshape(shape)	Reorganize this array into a new shape.
rms([axis, where])	The root-mean-square of this array along the given axes.
std([axis, where])	The standard deviation of this array along the given axes.
sum([axis, where])	The sum of each element of this array along the given axes.
to(unit[, equivalencies, copy])	Convert this array to a new unit.
to_string([prefix, multiline])	Convert this array instance to a string representation.
to_string_array([format_value, format_unit, ...])	Convert to an array of strings where each string has an appropriately-formatted unit attached to the value.
transpose([axes])	Reorder the axes of this array to the given sequence.
var([axis, where])	The variance of this array along the given axes.
vmr([axis, where])	The variance-to-mean ratio of this array along the given axes.
volume_cell(axis)	Computes the n-dimensional volume of each cell formed by interpreting this array as a logically-rectangular grid of vertices.

Inheritance Diagram

Parameters:

• pixel (AbstractCartesian2dVectorArray)

• time_start (Time | AbstractScalar)

• timedelta (Quantity | AbstractScalar)

• timedelta_requested (Quantity | AbstractScalar)

• serial_number (None | str | AbstractScalar)

• run_mode (None | str | AbstractScalar)

• status (None | str | AbstractScalar)

• voltage_fpga_vccint (Quantity | AbstractScalar)

• voltage_fpga_vccaux (Quantity | AbstractScalar)

• voltage_fpga_vccbram (Quantity | AbstractScalar)

• temperature_fpga (Quantity | AbstractScalar)

• temperature_adc_1 (Quantity | AbstractScalar)

• temperature_adc_2 (Quantity | AbstractScalar)

• temperature_adc_3 (Quantity | AbstractScalar)

• temperature_adc_4 (Quantity | AbstractScalar)

classmethod from_cartesian_nd(array, like=None)

Construct a new instance of this class using an instance of named_arrays.CartesianNdVectorArray.

Parameters:

• array (CartesianNdVectorArray) – The \(n\)-dimensional cartesian vector to convert.

• like (None | AbstractExplicitVectorArray) – A reference instance of the result. This is needed if the resulting class has components that are themselves vectors.

Return type:

AbstractExplicitVectorArray

classmethod from_components(components)

Construct a new instance of this class using a dict of components.

Parameters:

components (dict[str, AbstractArray]) – A dict of component names and values. The keys of the dict must match the names of the components in this class.

Return type:

AbstractExplicitVectorArray

classmethod from_scalar(scalar, like=None)

Convert a scalar (an instance of named_arrays.AbstractScalar) into a vector.

Parameters:

• scalar (AbstractScalar)

• like (None | AbstractExplicitVectorArray)

Return type:

Self

classmethod from_scalar_array(a, like=None)

Constructs a new version of this array using a as the underlying data.

Parameters:

• a (None | float | Quantity | AbstractArray) – Anything that can be coerced into an instance of named_arrays.AbstractScalarArray.

• like (None | AbstractExplicitVectorArray) – Optional reference object. If provided, the result will be defined by this object.

Return type:

AbstractExplicitVectorArray

add_axes(axes)

Add new singleton axes to this array.

Parameters:

• axes (str | Sequence[str]) – Either a single axis name or a sequence of axis names add to this array.

• self (Self)

Return type:

AbstractExplicitVectorArray

See also

named_arrays.add_axes()

A functional version of this method.

all(axis=None, where=<no value>)

Return True if all of the elements along the given axes are True.

Parameters:

• axis (None | str | Sequence[str]) – The logical axis or axes along which the operation is computed.

• where (Self) – An optional mask which selects which elements to be considered.

• self (Self)

Return type:

Self

See also

numpy.all()

A functional version of this method.

any(axis=None, where=<no value>)

Return True if any of the elements along the given axes are True.

Parameters:

• axis (None | str | Sequence[str]) – The logical axis or axes along which the operation is computed.

• where (Self) – An optional mask which selects which elements to be considered.

• self (Self)

Return type:

Self

See also

numpy.any()

A functional version of this method.

astype(dtype, order='K', casting='unsafe', subok=True, copy=True)

Copy of the array cast to a specific data type.

Equivalent to numpy.ndarray.astype().

Parameters:

• self (Self)

• dtype (str | dtype | Type | dict[str, str | dtype | Type])

• order (str)

• subok (bool)

• copy (bool)

Return type:

Self

broadcast_to(shape, append=False)

A new view of this array with the specified shape.

Parameters:

• shape (dict[str, int]) – The shape of the new array.

• append (bool) – If True, shape will be appended to the current shape of this array before broadcasting.

• self (Self)

Return type:

Self

See also

named_arrays.broadcast_to()

A functional version of this method.

cell_centers(axis=None, random=False)

Convert an array from cell vertices to cell centers.

Parameters:

• axis (None | str | Sequence[str]) – The axes of the array to average over.

• random (bool) – If true, select a random point within each cell instead of the geometric center.

Return type:

AbstractExplicitArray

combine_axes(axes=None, axis_new=None)

Combine some of the axes of the array into a single new axis.

Parameters:

• axes (Sequence[str]) – The axes to combine into a new axis

• axis_new (str) – The name of the new axis

• self (Self)

Return type:

Array with the specified axes combined

copy()

Create a deep copy of this array.

Parameters:

self (Self)

Return type:

Self

copy_shallow()

Create a shallow copy of this array.

Parameters:

self (Self)

Return type:

Self

index(value, axis=None)

Parameters:

• self (Self)

• value (Self)

• axis (None | str | Sequence[str])

Return type:

dict[str, Self]

index_secant(value, axis=None)

Parameters:

• self (Self)

• value (Self)

• axis (None | str | Sequence[str])

Return type:

dict[str, Self]

interp_linear(item)

Linearly interpolate this array to find its value at the given fractional index.

Parameters:

• item (dict[str, Self]) – A fractional index at which to evaluate the array.

• self (Self)

Return type:

Self

max(axis=None, initial=<no value>, where=<no value>)

The maximum value of this array along the given axes.

Parameters:

• axis (None | str | Sequence[str]) – The logical axis or axes along which the operation is computed.

• initial (_Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | complex | bytes | str | _NestedSequence[complex | bytes | str]) – The initial value of the minimum, required if where provided.

• where (Self) – An optional mask which selects which elements to be considered.

• self (Self)

Return type:

Self

See also

numpy.max()

A functional version of this method.

mean(axis=None, where=<no value>)

The mean value of this array along the given axes.

Parameters:

• axis (None | str | Sequence[str]) – The logical axis or axes along which the operation is computed.

• where (Self) – An optional mask which selects which elements to be considered.

• self (Self)

Return type:

Self

See also

numpy.mean()

A functional version of this method.

median(axis=None)

The median value of this array along the given axes.

Parameters:

axis (None | str | Sequence[str]) – The logical axis or axes along which the operation is computed.

See also

numpy.median()

A functional version of this method.

min(axis=None, initial=<no value>, where=<no value>)

The minimum value of this array along the given axes.

Parameters:

• axis (None | str | Sequence[str]) – The logical axis or axes along which the operation is computed.

• initial (_Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | complex | bytes | str | _NestedSequence[complex | bytes | str]) – The initial value of the minimum, required if where provided.

• where (Self) – An optional mask which selects which elements to be considered.

• self (Self)

Return type:

Self

See also

numpy.min()

A functional version of this method.

ndindex(axis_ignored=None)

An iterator that yields the index of each element of this array.

Parameters:

• axis_ignored (None | str | Sequence[str]) – The of the array to ignore when generating the iterator.

• self (Self)

Return type:

Iterator[dict[str, int]]

See also

named_arrays.ndindex()

A functional version of this method.

percentile(q, axis=None, out=None, overwrite_input=False, method='linear', keepdims=False)

The requested percentile of this array along the given axes.

Parameters:

• q (int | float | Quantity | Self) – The percentile to compute.

• axis (None | str | Sequence[str]) – The logical axis or axes along which the operation is computed.

• out (None | Self) – An optional output array in which to place the result.

• overwrite_input (bool) – Whether to overwrite the input array.

• method (str) – How to interpolate the result.

• keepdims (bool) – A boolean flag indicating whether to keep the reduced dimensions.

• self (Self)

See also

numpy.percentile()

A functional version of this method.

ptp(axis=None)

The peak-to-peak value of this array along the given axes.

Parameters:

• axis (None | str | Sequence[str]) – The logical axis or axes along which the operation is computed.

• self (Self)

Return type:

Self

See also

numpy.ptp()

A functional version of this method.

replace(**changes)

A method version of dataclasses.replace() for named arrays.

Parameters:

changes – The fields of the dataclass to be overwritten

Return type:

Self

reshape(shape)

Reorganize this array into a new shape.

Parameters:

• shape (dict[str, int]) – The new shape of the array, must be compatible with this array.

• self (Self)

Return type:

Self

rms(axis=None, where=<no value>)

The root-mean-square of this array along the given axes.

Parameters:

• axis (None | str | Sequence[str]) – The logical axis or axes along which the operation is computed.

• where (Self) – An optional mask which selects which elements to be considered.

• self (Self)

Return type:

Self

std(axis=None, where=<no value>)

The standard deviation of this array along the given axes.

Parameters:

• axis (None | str | Sequence[str]) – The logical axis or axes along which the operation is computed.

• where (Self) – An optional mask which selects which elements to be considered.

• self (Self)

Return type:

Self

See also

numpy.std()

A functional version of this method.

sum(axis=None, where=<no value>)

The sum of each element of this array along the given axes.

Parameters:

• axis (None | str | Sequence[str]) – The logical axis or axes along which the operation is computed.

• where (Self) – An optional mask which selects which elements to be considered.

• self (Self)

Return type:

Self

See also

numpy.sum()

A functional version of this method.

to(unit, equivalencies=[], copy=True)

Convert this array to a new unit.

Equivalent to astropy.units.Quantity.to().

Parameters:

• unit (UnitBase | dict[str, None | UnitBase]) – New unit of the returned array

• equivalencies (None | list[tuple[Unit, Unit]]) – A list of equivalence pairs to try if the units are not directly convertible.

• copy (bool) – Boolean flag controlling whether to copy the array.

• self (Self)

Return type:

AbstractExplicitVectorArray

to_string(prefix=None, multiline=None)

Convert this array instance to a string representation.

Parameters:

• prefix (None | str) – the length of this string is used to align the output

• multiline (None | bool) – flag which controls if the output should be spread over multiple lines.

Return type:

array represented as a str

to_string_array(format_value='%.2f', format_unit='latex_inline', pad_unit='$\\,$')

Convert to an array of strings where each string has an appropriately-formatted unit attached to the value.

Parameters:

• format_value (str) – The string used to format the numeric value of each element.

• format_unit (str) – The string used to format the units of each element.

• pad_unit (str) – The string used to add horizontal space between the value and unit.

Return type:

AbstractExplicitVectorArray

transpose(axes=None)

Reorder the axes of this array to the given sequence.

Parameters:

• axes (None | Sequence[str]) – The new axis ordering of this array.

• self (Self)

Return type:

Self

See also

numpy.transpose()

The numpy version of this method.

var(axis=None, where=<no value>)

The variance of this array along the given axes.

Parameters:

• axis (None | str | Sequence[str]) – The logical axis or axes along which the operation is computed.

• where (Self) – An optional mask which selects which elements to be considered.

• self (Self)

Return type:

Self

See also

numpy.var()

A functional version of this method.

vmr(axis=None, where=<no value>)

The variance-to-mean ratio of this array along the given axes.

Parameters:

• axis (None | str | Sequence[str]) – The logical axis or axes along which the operation is computed.

• where (Self) – An optional mask which selects which elements to be considered.

volume_cell(axis)

Computes the n-dimensional volume of each cell formed by interpreting this array as a logically-rectangular grid of vertices.

Note that this method is usually only used for sorted arrays.

If self is a scalar, this method computes the length of each edge, and is equivalent to numpy.diff(). If self is a 2d vector, this method computes the area of each quadrilateral, and if self is a 3d vector, this method computes the volume of each cuboid.

Parameters:

axis (None | str | Sequence[str]) – The axis or axes defining the logically-rectangular grid. If self is a physical scalar, there should only be one axis. If self is a physical vector, there should be one axis for each component of the vector.

Return type:

AbstractScalar

property axes: tuple[str, ...]

A tuple of str representing the names of each dimension of this array.

Must have the same length as the number of dimensions of this array.

property axes_flattened: str

Combine axes into a single str.

This is useful for functions like numpy.flatten() which returns an array with only one dimension.

property broadcasted: AbstractExplicitArray

if this array has multiple components, broadcast them against each other.

Equivalent to a.broadcast_to(a.shape).

property cartesian_nd: AbstractCartesianNdVectorArray

Convert any instance of AbstractVectorArray to an instance of AbstractCartesianNdVectorArray

property components: dict[str, int | float | complex | ndarray | Quantity | AbstractArray]

The vector components of this array expressed as a dict where the keys are the names of the component.

property entries: dict[str, int | float | complex | ndarray | Quantity | AbstractArray]

The scalar entries that compose this object.

property explicit: AbstractExplicitVectorArray

Converts this array to an instance of named_arrays.AbstractExplicitArray.

property indices: dict[str, ScalarArrayRange]

Compute the index of each element of this array.

See also

named_arrays.indices()

A functional version of this method.

property length: AbstractScalar

L2-norm of this array.

property matrix: AbstractMatrixArray

Cast this vector into its matrix representation.

property ndim: int

Number of dimensions of the array. Equivalent to numpy.ndarray.ndim.

property normalized: Self

Return a normalized copy of this vector, where length is unity.

pixel: AbstractCartesian2dVectorArray = <dataclasses._MISSING_TYPE object>

The indices of each pixel in the image.

property prototype_vector: AbstractExplicitVectorArray

Return vector of same type with all components zeroed.

run_mode: None | str | AbstractScalar = None

The Run Mode of the camera when each image was captured.

serial_number: None | str | AbstractScalar = None

The serial number of the camera that captured each image.

property shape: dict[str, int]

The number of elements along each axis of the array. Analogous to numpy.ndarray.shape but represented as a dict where the keys are the axis names and the values are the axis sizes.

property size: int

Total number of elements in the array. Equivalent to numpy.ndarray.size

status: None | str | AbstractScalar = None

The status of the camera while each image was being captured.

temperature_adc_1: Quantity | AbstractScalar = 0

Temperature 1 of the ADC when each image was captured.

temperature_adc_2: Quantity | AbstractScalar = 0

Temperature 2 of the ADC when each image was captured.

temperature_adc_3: Quantity | AbstractScalar = 0

Temperature 3 of the ADC when each image was captured.

temperature_adc_4: Quantity | AbstractScalar = 0

Temperature 4 of the ADC when each image was captured.

temperature_fpga: Quantity | AbstractScalar = 0

The temperature of the FPGA when each image was captured.

property time: Time | ScalarArray

The time in UTC at the midpoint of the exposure.

property time_end: Time | ScalarArray

The time in UTC at the end of the exposure.

time_start: Time | AbstractScalar = <dataclasses._MISSING_TYPE object>

The time in UTC at the start of the exposure.

timedelta: Quantity | AbstractScalar = <dataclasses._MISSING_TYPE object>

The measured exposure time of each image.

timedelta_requested: Quantity | AbstractScalar = <dataclasses._MISSING_TYPE object>

The requested exposure time of each image.

property type_abstract: Type[Self]

The named_arrays.AbstractArray type corresponding to this array.

property type_explicit: Type[Self]

The named_arrays.AbstractExplicitArray type corresponding to this array.

property type_matrix: Type[AbstractCartesianMatrixArray]

The corresponding named_arrays.AbstractMatrixArray class

um = <module 'numpy._core.umath' from '/home/docs/checkouts/readthedocs.org/user_builds/msfc-ccd/envs/stable/lib/python3.11/site-packages/numpy/_core/umath.py'>

property value: AbstractExplicitVectorArray

Returns a new array with its units removed, if they exist.

voltage_fpga_vccaux: Quantity | AbstractScalar = 0

The VCCAUX voltage of the FPGA when each image was captured.

voltage_fpga_vccbram: Quantity | AbstractScalar = 0

The VCCBRAM voltage of the FPGA when each image was captured.

voltage_fpga_vccint: Quantity | AbstractScalar = 0

The VCCINT voltage of the FPGA when each image was captured.