vis133m.py API¶

`bh_molecule.instruments.vis133m` ¶

Attributes¶

Classes¶

`FCPShape` ¶

Bases: NamedTuple

Attributes¶

`C: int` `instance-attribute` ¶

`F: int` `instance-attribute` ¶

`P: int` `instance-attribute` ¶

`Vis133M` ¶

Minimal loader/processor for VIS-1.33 m data with per-channel wavecal.

This class wraps a FITS data cube produced by the VIS-1.33 m instrument and a per-channel wavelength calibration (CSV or analytic formula). It provides convenient accessors and plotting helpers for frames, channels and pixels, plus simple dark subtraction and time-axis helpers.

Parameters:

Name	Type	Description	Default
`fits_path`	`str`	Path to a FITS file containing a 3D data cube with shape `(F, C, P)` (frames, channels, pixels).	required
`wavecal`	`str \| None`	Path to a CSV file containing per-channel wavelength calibration, or None if using explicit formula (slopes, intercepts). The first column must contain channel indices (0-based preferred, 1-based accepted). Remaining columns are wavelength values [nm] per pixel.	`None`
`wavecal_mode`	`(formula, csv)`	"formula" (default): compute wavelength from λ(x) = a*x + b + Δλ per channel. If wavecal is a path, load CSV and convert to formulas. "csv": use CSV table directly for pixel→wavelength lookup (legacy).	`"formula"`
`scale`	`float`	Multiplicative scale factor applied to the cube data (default 1.0).	`1.0`
`slopes`	`ndarray \| None`	Per-channel slopes [nm/pixel] for formula mode. Required when wavecal=None and wavecal_mode="formula".	`None`
`intercepts`	`ndarray \| None`	Per-channel intercepts [nm] for formula mode. Required when wavecal=None and wavecal_mode="formula".	`None`

Attributes:

Name	Type	Description
`cube`	`ndarray`	The data cube of shape `(F, C, P)`.
`wl_nm`	`ndarray`	Per-channel wavelength array with shape `(C, P)` in nanometres.
`wavecal_shift`	`float`	Global wavelength shift Δλ [nm] applied in formula mode.
`header`	`dict`	FITS header converted to a dict.
`exptime`	`float \| None`	Exposure time read from the FITS header when available.
`time_s`	`ndarray \| None`	Optional time vector in seconds of length `F`. If unset, frame indices are used for plotting/time axes.

Attributes¶

`axis_legend: dict[str, str]` `property` ¶

Mapping of axis short names to human-readable descriptions.

Returns:

Type	Description
`dict`	Mapping with keys `'F'`, `'C'`, `'P'` describing the axes.

`cube = cube` `instance-attribute` ¶

`exptime = self.header.get('EXPTIME') or self.header.get('EXPOSURE')` `instance-attribute` ¶

`filename = fits_path` `instance-attribute` ¶

`header = dict(hdu.header)` `instance-attribute` ¶

`scale = float(scale)` `instance-attribute` ¶

`shape` `property` ¶

Return the shape of the internal data cube.

Returns:

Type	Description
`tuple`	Shape `(F, C, P)` where `F` is frames, `C` is channels and `P` is pixels (wavelength samples).

`shape_fcp: FCPShape` `property` ¶

Named shape with axis meanings.

Returns:

Type	Description
`FCPShape`	Named tuple containing `F`, `C` and `P` describing the number of frames, channels and pixels respectively.

`time_s = None` `instance-attribute` ¶

`wavecal_shift = 0.0` `instance-attribute` ¶

`wl_nm = wl_csv` `instance-attribute` ¶

Functions¶

`apply_cw(cw_nm: float, *, line_name: str | None = None, line_wavelength: float | None = None, peak_pixel: int | None = None, frame_used: int | None = None) -> None` ¶

Apply central wavelength and update the wavelength axis.

Regenerates the wavelength calibration from the stored polynomial and the given CW. Updates calibration metadata. Optionally store line info (e.g. from :meth:estimate_cw_from_line) for the report.

Parameters:

Name	Type	Description	Default
`cw_nm`	`float`	Central wavelength in nm.	required
`line_name`	`str \| None`	Name of the reference line (e.g. `"H_gamma"`) for the report.	`None`
`line_wavelength`	`float \| None`	Wavelength of the line in nm for the report.	`None`
`peak_pixel`	`int \| None`	Pixel index used for the estimate (for the report).	`None`
`frame_used`	`int \| None`	Frame index used (for the report).	`None`

`band(nm_range: tuple[float, float], subtract_dark: bool = True) -> np.ndarray` ¶

Sum signal within a wavelength band (per-channel) returning an image of shape (F, C).

Parameters:

Name	Type	Description	Default
`nm_range`	`tuple of float`	`(lo, hi)` wavelength range in nanometres (inclusive).	required
`subtract_dark`	`bool`	If True and a dark is configured via `set_dark`, subtract the dark from the resulting image (default True).	`True`

Returns:

Type	Description
`ndarray`	Summed image of shape `(F, C)` in the same units as the cube multiplied by the current `scale`.

`calibration_report() -> None` ¶

Print the wavelength calibration report (for instances from from_files).

`channel_stack(channel: int, *, zero_min: bool | None = None) -> np.ndarray` ¶

Return the time×pixel stack for a single channel.

Parameters:

Name	Type	Description	Default
`channel`	`int`	Channel index (0-based).	required

Returns:

Type	Description
`ndarray`	Array of shape `(F, P)` containing the stack for the channel.

`compare_calibration_csv_vs_formula(channel: int) -> tuple[float, float]` ¶

Compare CSV and formula calibration for a channel.

Requires that calibration was loaded from CSV. When in csv mode, formula is computed from the same CSV for comparison. Compares only over the pixel range present in the CSV (cube may have more).

Returns:

Name	Type	Description
`max_abs_delta`	`float`	max \|λ_csv - λ_formula\| over pixels.
`rmse`	`float`	Root-mean-square error between CSV and formula.

`compute_wavelength_shift_from_peaks(peak_pixel_old: float, peak_pixel_new: float, channel: int = 0) -> float` ¶

Compute wavelength shift Δλ from pixel shifts and channel dispersion.

Δλ = a_c * (peak_pixel_new - peak_pixel_old)

Parameters:

Name	Type	Description	Default
`peak_pixel_old`	`float`	Peak pixel in reference (old) dataset.	required
`peak_pixel_new`	`float`	Peak pixel in new dataset.	required
`channel`	`int`	Channel index for slope (default 0).	`0`

Returns:

Name	Type	Description
`delta_lambda`	`float`	Wavelength shift in nm.

`estimate_cw_from_line(peak_pixel: int, *, wavelength: float | None = None, line: str | None = None) -> float` ¶

Estimate central wavelength from a known line at a given pixel.

Does not modify the instance. Use :meth:apply_cw to apply the result.

Parameters:

Name	Type	Description	Default
`peak_pixel`	`int`	Pixel index (data coordinates) where the line is observed.	required
`wavelength`	`float \| None`	Wavelength of the line in nm. One of `wavelength` or `line` must be provided.	`None`
`line`	`str \| None`	Name of the line (e.g. `"H_gamma"`) looked up in :data:`bh_molecule.constants.BALMER_LINES_NM`. One of `wavelength` or `line` must be provided.	`None`

Returns:

Type	Description
`float`	Estimated CW in nm.

`explain() -> None` ¶

Print a short human-readable description of the data axes.

This helper is convenient in interactive sessions or notebooks.

`frame_image(frame: int, *, zero_min: bool | None = None) -> np.ndarray` ¶

Return the channel×pixel image for a single frame.

Parameters:

Name	Type	Description	Default
`frame`	`int`	Frame index (0-based).	required

Returns:

Type	Description
`ndarray`	Array of shape `(C, P)` corresponding to the requested frame.

`from_files(fits_path: str, *, cw: float | None = None, line: str | float | None = None, scale: float = 1.0, verbose: bool = False) -> Vis133M` `classmethod` ¶

Create a Vis133M instance with polynomial wavelength calibration from JSON.

Loads the FITS cube and the wavecal JSON from package resources. CW must be set explicitly: pass cw (nm) to use a value directly, or pass line (name or wavelength) to record the reference line for later use with :meth:estimate_cw_from_line and :meth:apply_cw. If neither is provided, data are loaded with dispersion only; call :meth:apply_cw after estimating CW.

Parameters:

Name	Type	Description	Default
`fits_path`	`str`	Path to a FITS file containing a 3D data cube (F, C, P).	required
`cw`	`float \| None`	Central wavelength in nm. If provided, calibration is applied immediately. Mutually exclusive with `line`.	`None`
`line`	`str \| float \| None`	Reference line: string (e.g. `"H_gamma"`) looked up in :data:`bh_molecule.constants.BALMER_LINES_NM`, or float (wavelength in nm). Stored for use with :meth:`estimate_cw_from_line`; does not apply CW by itself.	`None`
`scale`	`float`	Multiplicative scale factor (default 1.0).	`1.0`
`verbose`	`bool`	If True, print the calibration report after loading (default False).	`False`

Returns:

Type	Description
`Vis133M`	Instance. If `cw` was provided, wavelength axis is calibrated.

`map_band(nm_range: tuple[float, float], *, subtract_dark: bool = True) -> np.ndarray` ¶

Sum signal within a wavelength band (per-channel) returning (F, C).

Parameters:

Name	Type	Description	Default
`nm_range`	`tuple of float`	`(lo, hi)` wavelength range in nanometres (inclusive).	required
`subtract_dark`	`bool`	If True subtract a configured dark (default True).	`True`

Returns:

Type	Description
`ndarray`	Summed image of shape `(F, C)`.

`map_pixel_range(start: int, stop: int, *, subtract_dark: bool = True) -> np.ndarray` ¶

Sum over a pixel window returning an (F, C) image.

Parameters:

Name	Type	Description	Default
`start`	`int`	Start pixel index (inclusive).	required
`stop`	`int`	Stop pixel index (exclusive).	required
`subtract_dark`	`bool`	If True subtract a configured dark (default True).	`True`

Returns:

Type	Description
`ndarray`	Summed image of shape `(F, C)`.

`measure_peak(channel: int, pixel_window: tuple[int, int] | None = None, average_frames: bool = True) -> tuple[float, float, float] | None` ¶

Fit strongest peak (e.g. H-γ) in a channel spectrum.

Parameters:

Name	Type	Description	Default
`channel`	`int`	Channel index.	required
`pixel_window`	`tuple of int`	(start, stop) pixel indices. If None, use full spectrum.	`None`
`average_frames`	`bool`	If True, average over frames before fitting (default True).	`True`

Returns:

Name	Type	Description
`result`	`tuple or None`	(peak_pixel, peak_amplitude, sigma) if fit succeeds.

`pick_frame(method: str = 'max') -> int` ¶

Return the frame index with strongest total signal.

Parameters:

Name	Type	Description	Default
`method`	`str`	Only `"max"` is supported: frame with maximum sum over (channel, pixel).	`'max'`

Returns:

Type	Description
`int`	Frame index (0-based).

`pixel_map(pixel: int) -> np.ndarray` ¶

Return the frame×channel map at a fixed detector pixel.

Parameters:

Name	Type	Description	Default
`pixel`	`int`	Pixel index (0-based).	required

Returns:

Type	Description
`ndarray`	Array of shape `(F, C)` containing values at the given pixel.

`plot_band_map(nm_range: tuple[float, float], *, ax=None, cmap=None, cbar_label='intensity', channel_line: int | None = None, require_time: bool = False, subtract_dark: bool = True, log_scale: bool = False, vmin: float | None = None, vmax: float | None = None, colorbar: bool = True)` ¶

Plot the result of map_band(nm_range).

Parameters:

Name	Type	Description	Default
`nm_range`	`tuple of float`	`(lo, hi)` wavelength range in nanometres.	required

Returns:

Type	Description
`Axes`	The axes containing the image.
`See Also map_band, _plot_fc`

`plot_channel_stack(channel: int, *, ax=None, cmap=None, cbar_label='intensity', time_line: float | None = None, require_time: bool = False, log_scale: bool = False, vmin: float | None = None, vmax: float | None = None)` ¶

Plot a channel stack (time × wavelength) as an image.

Parameters:

Name	Type	Description	Default
`channel`	`int`	Channel index (0-based) to plot.	required
`ax`	`Axes \| None`	Axes to plot into. If None the current axes are used.	`None`
`cmap`	`str \| Colormap \| None`	Colormap to use for the image.	`None`
`cbar_label`	`str`	Label for the colorbar (default "intensity").	`'intensity'`
`time_line`	`float \| None`	Optional vertical line (x coordinate in time units) to indicate a time-of-interest.	`None`
`require_time`	`bool`	If True require an explicit time vector (raise if unset).	`False`

Returns:

Type	Description
`Axes`	The axes containing the image.

`plot_pixel_map(pixel: int, *, ax=None, cmap=None, cbar_label='intensity', channel_line: int | None = None, require_time: bool = False, log_scale: bool = False, vmin: float | None = None, vmax: float | None = None)` ¶

Plot a frame×channel image for a fixed pixel index.

Parameters:

Name	Type	Description	Default
`pixel`	`int`	Pixel index (0-based).	required
`ax`	`Axes \| None`	Axes to plot into. If None the current axes are used.	`None`
`cmap`	`str \| Colormap \| None`	Colormap to use for the image.	`None`
`cbar_label`	`str`	Label for the colorbar (default "intensity").	`'intensity'`
`channel_line`	`int \| None`	Optional horizontal line indicating a channel of interest.	`None`
`require_time`	`bool`	If True require an explicit time vector (raise if unset).	`False`

Returns:

Type	Description
`Axes`	The axes containing the image.

`plot_pixel_range(start: int, stop: int, *, ax=None, cmap=None, cbar_label='intensity', channel_line: int | None = None, require_time: bool = False, subtract_dark: bool = True, log_scale: bool = False, vmin: float | None = None, vmax: float | None = None)` ¶

Plot the result of map_pixel_range(start, stop).

Parameters:

Name	Type	Description	Default
`start`	`int`	Start pixel index (inclusive).	required
`stop`	`int`	Stop pixel index (exclusive).	required

Returns:

Type	Description
`Axes`	The axes containing the image.

`plot_spectrum(frame: int, channel: int, ax=None, *, zero_min: bool | None = None)` ¶

Plot the spectrum at a given frame and channel using Matplotlib.

Parameters:

Name	Type	Description	Default
`frame`	`int`	Frame index (0-based).	required
`channel`	`int`	Channel index (0-based).	required
`ax`	`Axes \| None`	Axes to plot into. If None the current axes are used.	`None`

Returns:

Type	Description
`Axes`	The axes containing the plot.

`plot_spectrum_pixels(frame: int, channel: int | None = None, *, reduce: str = 'sum_channels', show_peaks: bool = True)` ¶

Plot spectrum with pixel index on the x-axis (for CW inspection).

Uses Plotly. Optionally marks detected peaks.

Parameters:

Name	Type	Description	Default
`frame`	`int`	Frame index.	required
`channel`	`int \| None`	If provided, plot that channel; otherwise use `reduce`.	`None`
`reduce`	`str`	When `channel` is None, `"sum_channels"` (default) sums channels.	`'sum_channels'`
`show_peaks`	`bool`	If True, detect peaks with scipy.signal.find_peaks and mark them.	`True`

Returns:

Type	Description
`Figure`

`plot_spectrum_plotly(frame: int, channel: int, *, sort_wavelength: bool = True, line_shape: str = 'linear', zero_min: bool | None = None, theme: str | None = None, bg: str = '#44423e', fg: str = '#eaeaea')` ¶

Return an interactive Plotly figure for a spectrum.

Parameters:

Name	Type	Description	Default
`frame`	`int`	Frame index (0-based).	required
`channel`	`int`	Channel index (0-based).	required
`sort_wavelength`	`bool`	If True, sort the wavelength vector to be monotonic for better interactive behaviour (default True).	`True`
`line_shape`	`str`	Plotly line shape (e.g. 'linear', 'spline').	`'linear'`
`theme`	`(dark, light, None)`	If "dark", apply a dark layout to the figure. If None, inherit Plotly's default.	`"dark","light",None`
`bg`	`str`	Background color for dark theme.	`'#44423e'`
`fg`	`str`	Foreground color for dark theme.	`'#eaeaea'`

Returns:

Type	Description
`Figure`	Interactive figure containing the spectrum.

Raises:

Type	Description
`ImportError`	If Plotly is not available.

Example

fig = s26.plot_spectrum_plotly(38, 36) fig.show()

`print_header()` ¶

Print all FITS header fields.

`set_baseline_zero(enable: bool = True) -> None` ¶

Enable/disable per-spectrum minimum subtraction.

When enabled, each spectrum row (across pixels) will be shifted so its minimum is zero in methods that return spectra-like arrays.

Parameters:

Name	Type	Description	Default
`enable`	`bool`	If True, subtract per-row minima from spectra results. If False, disable this behaviour.	`True`

`set_dark(*, frame: int | None = None, channel: int | None = None, vector=None, value: float | None = None)` ¶

Configure dark subtraction behaviour.

Dark can be specified in several ways: - vector: an array-like that will be used directly as the dark correction (broadcasting rules apply). - value: a scalar dark value subtracted from all pixels. - frame and channel: record a reference index; subtraction will subtract the value found at that (frame, channel) location when applied.

Parameters:

Name	Type	Description	Default
`frame`	`int \| None`	Frame index used for index-based dark (with `channel`).	`None`
`channel`	`int \| None`	Channel index used for index-based dark (with `frame`).	`None`
`vector`	`array - like \| None`	Direct dark vector/array to use for subtraction.	`None`
`value`	`float \| None`	Scalar dark value to subtract.	`None`

`set_scale(scale: float)` ¶

Set the global multiplicative scale applied to the cube.

Parameters:

Name	Type	Description	Default
`scale`	`float`	New scale factor.	required

`set_time(t)` ¶

Set an explicit time vector for the frames.

Parameters:

Name	Type	Description	Default
`t`	`array - like`	Time vector (seconds) of length `F` where `F` is the number of frames in the cube. Must be non-decreasing and contain finite values.	required

Raises:

Type	Description
`ValueError`	If the vector length does not match `F`, contains non-finite values, or is not non-decreasing.

`set_time_linspace(start_s: float, stop_s: float)` ¶

Set time_s to a linearly spaced vector between two times.

Parameters:

Name	Type	Description	Default
`start_s`	`float`	Start time in seconds.	required
`stop_s`	`float`	Stop time in seconds.	required

`set_time_period(period_s: float, start_s: float = 0.0)` ¶

Set time_s assuming a constant frame period.

Parameters:

Name	Type	Description	Default
`period_s`	`float`	Time between successive frames in seconds.	required
`start_s`	`float`	Time of the first frame (default 0.0 s).	`0.0`

`set_wavecal_shift(delta_lambda_nm: float) -> None` ¶

Set the global wavelength shift applied in formula mode.

λ'(x) = a*x + b + Δλ

Parameters:

Name	Type	Description	Default
`delta_lambda_nm`	`float`	Shift in nanometres.	required

`spectrum(frame: int, channel: int | None = None, *, reduce: str = 'sum_channels', zero_min: bool | None = None) -> tuple[np.ndarray, np.ndarray]` ¶

Return (x-axis, intensity) for a frame.

Parameters:

Name	Type	Description	Default
`frame`	`int`	Frame index (0-based).	required
`channel`	`int \| None`	If provided, return that channel's spectrum. If None, combine channels according to `reduce`.	`None`
`reduce`	`str`	When `channel` is None, how to combine channels: `"sum_channels"` (default) sums across channels.	`'sum_channels'`
`zero_min`	`bool \| None`	If True, subtract per-row minimum. If None, use `set_baseline_zero` setting.	`None`

Returns:

Type	Description
`tuple of ndarray`	If `channel` is provided: `(wavelengths, signal)` in nm and scaled counts. If `channel` is None: `(pixels, intensity)` with pixel indices and combined intensity (for inspection).

`summary()` ¶

Print a compact summary of the acquisition.

Functions¶

`parse_vis133m_header(header: dict) -> dict` ¶

Extract useful acquisition metadata from a Vis133M FITS header.

Loader and processor for VIS-1.33 m FITS data cubes with per-channel wavelength calibration.

vis133m.py API¶

bh_molecule.instruments.vis133m ¶

Attributes¶

Classes¶

FCPShape ¶

Attributes¶

C: int instance-attribute ¶

F: int instance-attribute ¶

P: int instance-attribute ¶

Vis133M ¶

Attributes¶

axis_legend: dict[str, str] property ¶

cube = cube instance-attribute ¶

exptime = self.header.get('EXPTIME') or self.header.get('EXPOSURE') instance-attribute ¶

filename = fits_path instance-attribute ¶

header = dict(hdu.header) instance-attribute ¶

scale = float(scale) instance-attribute ¶

shape property ¶

shape_fcp: FCPShape property ¶

time_s = None instance-attribute ¶

wavecal_shift = 0.0 instance-attribute ¶

wl_nm = wl_csv instance-attribute ¶

Functions¶

apply_cw(cw_nm: float, *, line_name: str | None = None, line_wavelength: float | None = None, peak_pixel: int | None = None, frame_used: int | None = None) -> None ¶

band(nm_range: tuple[float, float], subtract_dark: bool = True) -> np.ndarray ¶

calibration_report() -> None ¶

channel_stack(channel: int, *, zero_min: bool | None = None) -> np.ndarray ¶

compare_calibration_csv_vs_formula(channel: int) -> tuple[float, float] ¶

compute_wavelength_shift_from_peaks(peak_pixel_old: float, peak_pixel_new: float, channel: int = 0) -> float ¶

estimate_cw_from_line(peak_pixel: int, *, wavelength: float | None = None, line: str | None = None) -> float ¶

explain() -> None ¶

frame_image(frame: int, *, zero_min: bool | None = None) -> np.ndarray ¶

from_files(fits_path: str, *, cw: float | None = None, line: str | float | None = None, scale: float = 1.0, verbose: bool = False) -> Vis133M classmethod ¶

map_band(nm_range: tuple[float, float], *, subtract_dark: bool = True) -> np.ndarray ¶

map_pixel_range(start: int, stop: int, *, subtract_dark: bool = True) -> np.ndarray ¶

measure_peak(channel: int, pixel_window: tuple[int, int] | None = None, average_frames: bool = True) -> tuple[float, float, float] | None ¶

pick_frame(method: str = 'max') -> int ¶

pixel_map(pixel: int) -> np.ndarray ¶

plot_band_map(nm_range: tuple[float, float], *, ax=None, cmap=None, cbar_label='intensity', channel_line: int | None = None, require_time: bool = False, subtract_dark: bool = True, log_scale: bool = False, vmin: float | None = None, vmax: float | None = None, colorbar: bool = True) ¶

plot_channel_stack(channel: int, *, ax=None, cmap=None, cbar_label='intensity', time_line: float | None = None, require_time: bool = False, log_scale: bool = False, vmin: float | None = None, vmax: float | None = None) ¶

plot_pixel_map(pixel: int, *, ax=None, cmap=None, cbar_label='intensity', channel_line: int | None = None, require_time: bool = False, log_scale: bool = False, vmin: float | None = None, vmax: float | None = None) ¶

plot_pixel_range(start: int, stop: int, *, ax=None, cmap=None, cbar_label='intensity', channel_line: int | None = None, require_time: bool = False, subtract_dark: bool = True, log_scale: bool = False, vmin: float | None = None, vmax: float | None = None) ¶

plot_spectrum(frame: int, channel: int, ax=None, *, zero_min: bool | None = None) ¶

plot_spectrum_pixels(frame: int, channel: int | None = None, *, reduce: str = 'sum_channels', show_peaks: bool = True) ¶

plot_spectrum_plotly(frame: int, channel: int, *, sort_wavelength: bool = True, line_shape: str = 'linear', zero_min: bool | None = None, theme: str | None = None, bg: str = '#44423e', fg: str = '#eaeaea') ¶

print_header() ¶

set_baseline_zero(enable: bool = True) -> None ¶

set_dark(*, frame: int | None = None, channel: int | None = None, vector=None, value: float | None = None) ¶

set_scale(scale: float) ¶

set_time(t) ¶

set_time_linspace(start_s: float, stop_s: float) ¶

set_time_period(period_s: float, start_s: float = 0.0) ¶

set_wavecal_shift(delta_lambda_nm: float) -> None ¶

spectrum(frame: int, channel: int | None = None, *, reduce: str = 'sum_channels', zero_min: bool | None = None) -> tuple[np.ndarray, np.ndarray] ¶

summary() ¶

Functions¶

parse_vis133m_header(header: dict) -> dict ¶

`bh_molecule.instruments.vis133m` ¶

`FCPShape` ¶

`C: int` `instance-attribute` ¶

`F: int` `instance-attribute` ¶

`P: int` `instance-attribute` ¶

`Vis133M` ¶

`axis_legend: dict[str, str]` `property` ¶

`cube = cube` `instance-attribute` ¶

`exptime = self.header.get('EXPTIME') or self.header.get('EXPOSURE')` `instance-attribute` ¶

`filename = fits_path` `instance-attribute` ¶

`header = dict(hdu.header)` `instance-attribute` ¶

`scale = float(scale)` `instance-attribute` ¶

`shape` `property` ¶

`shape_fcp: FCPShape` `property` ¶

`time_s = None` `instance-attribute` ¶

`wavecal_shift = 0.0` `instance-attribute` ¶

`wl_nm = wl_csv` `instance-attribute` ¶

`apply_cw(cw_nm: float, *, line_name: str | None = None, line_wavelength: float | None = None, peak_pixel: int | None = None, frame_used: int | None = None) -> None` ¶

`band(nm_range: tuple[float, float], subtract_dark: bool = True) -> np.ndarray` ¶

`calibration_report() -> None` ¶

`channel_stack(channel: int, *, zero_min: bool | None = None) -> np.ndarray` ¶

`compare_calibration_csv_vs_formula(channel: int) -> tuple[float, float]` ¶

`compute_wavelength_shift_from_peaks(peak_pixel_old: float, peak_pixel_new: float, channel: int = 0) -> float` ¶

`estimate_cw_from_line(peak_pixel: int, *, wavelength: float | None = None, line: str | None = None) -> float` ¶

`explain() -> None` ¶

`frame_image(frame: int, *, zero_min: bool | None = None) -> np.ndarray` ¶

`from_files(fits_path: str, *, cw: float | None = None, line: str | float | None = None, scale: float = 1.0, verbose: bool = False) -> Vis133M` `classmethod` ¶

`map_band(nm_range: tuple[float, float], *, subtract_dark: bool = True) -> np.ndarray` ¶

`map_pixel_range(start: int, stop: int, *, subtract_dark: bool = True) -> np.ndarray` ¶

`measure_peak(channel: int, pixel_window: tuple[int, int] | None = None, average_frames: bool = True) -> tuple[float, float, float] | None` ¶

`pick_frame(method: str = 'max') -> int` ¶

`pixel_map(pixel: int) -> np.ndarray` ¶

`plot_band_map(nm_range: tuple[float, float], *, ax=None, cmap=None, cbar_label='intensity', channel_line: int | None = None, require_time: bool = False, subtract_dark: bool = True, log_scale: bool = False, vmin: float | None = None, vmax: float | None = None, colorbar: bool = True)` ¶

`plot_channel_stack(channel: int, *, ax=None, cmap=None, cbar_label='intensity', time_line: float | None = None, require_time: bool = False, log_scale: bool = False, vmin: float | None = None, vmax: float | None = None)` ¶

`plot_pixel_map(pixel: int, *, ax=None, cmap=None, cbar_label='intensity', channel_line: int | None = None, require_time: bool = False, log_scale: bool = False, vmin: float | None = None, vmax: float | None = None)` ¶

`plot_pixel_range(start: int, stop: int, *, ax=None, cmap=None, cbar_label='intensity', channel_line: int | None = None, require_time: bool = False, subtract_dark: bool = True, log_scale: bool = False, vmin: float | None = None, vmax: float | None = None)` ¶

`plot_spectrum(frame: int, channel: int, ax=None, *, zero_min: bool | None = None)` ¶

`plot_spectrum_pixels(frame: int, channel: int | None = None, *, reduce: str = 'sum_channels', show_peaks: bool = True)` ¶

`plot_spectrum_plotly(frame: int, channel: int, *, sort_wavelength: bool = True, line_shape: str = 'linear', zero_min: bool | None = None, theme: str | None = None, bg: str = '#44423e', fg: str = '#eaeaea')` ¶

`print_header()` ¶

`set_baseline_zero(enable: bool = True) -> None` ¶

`set_dark(*, frame: int | None = None, channel: int | None = None, vector=None, value: float | None = None)` ¶

`set_scale(scale: float)` ¶

`set_time(t)` ¶

`set_time_linspace(start_s: float, stop_s: float)` ¶

`set_time_period(period_s: float, start_s: float = 0.0)` ¶

`set_wavecal_shift(delta_lambda_nm: float) -> None` ¶

`spectrum(frame: int, channel: int | None = None, *, reduce: str = 'sum_channels', zero_min: bool | None = None) -> tuple[np.ndarray, np.ndarray]` ¶

`summary()` ¶

`parse_vis133m_header(header: dict) -> dict` ¶