Functions
	all_same (a)

	apply_boxcar (a, half_width)
	apply a boxcar convolution of the given half_width to input array 'a'

	area_under_peak (spectrum, x_axis, x_index, boxcar_half_width=0)
	integrate the 'area under the curve' for the given spectrum and x_axis, using the peak centered on x_axis, with optional smoothing given the boxcar width.

	ascii_spectrum (spectrum, rows, cols, x_axis, x_unit)
	render a spectrum as ASCII-art

	check_admin ()

	clamp_to_int16 (n)

	clean_nan (a)

	coeffs_look_valid (coeffs, count=None)
	Can be used as a sanity-check for any set of coefficients.

	dict_get_norm (d, keys)
	Similar to dict.get(), but case-insensitive and normalizes-out spaces, underscores, periods and hyphens.

	dict_get_path (d, keys)
	Similar to dict.get(), but takes a list of keys to be traversed in sequence.

	dump (foo, indent=0)
	similar to Perl's Data.Dumper

	find_local_maxima (a, x_axis, center, tolerance=0)
	Given a spectrum (array 'a'), with an x_axis, a 'center' along that x_axis, and an allowed 'tolerance' (in same units as the x_axis), find the local maxima within 'tolerance' of 'center'.

	find_nearest_index (L, value)

	find_nearest_value (L, value)

	find_peak_feet_indices (spectrum, x_axis, x_index, boxcar_half_width=0)
	Given a spectrum and an x_axis, find the indexes of the left and right 'feet' of the peak centered on x_index.

	generate_excitation (wavelengths, wavenumbers)
	If we've loaded a CSV that had wavelength and wavenumber columns, but no metadata, use this to infer the excitation wavelength.

	generate_wavelengths (pixels, coeffs)
	expand 3rd-order wavelength polynomial into array of wavelengths

	generate_wavelengths_from_wavenumbers (excitation, wavenumbers)

	generate_wavenumbers (excitation, wavelengths, wavenumber_correction=0)
	convert wavelengths into Raman shifts in 1/cm wavenumbers from the given excitation wavelength

	get_pathnames_from_directory (rootdir, pattern=None, recursive=False)
	iterate down a directory, returning pathnames that match the given pattern

	interpolate_array (spectrum, old_axis, new_axis)
	Interpolate the passed spectrum over a fixed x-axis (e.g.

	interpolate_value (spectrum, old_axis, x)
	I might be making this more difficult than it needs to be.

	load_json (pathname)
	convenience wrapper to load a JSON file

	parabolic_approximation (pixel, x, y)
	Given an array of doubles and a peak index, use the peak and its two neighbors to form a parabola and return the interpolated maximum height of the parabola.

	peak_height_above_background (spectrum, x_axis, x_index, boxcar_half_width=0)

	pixel_to_wavelength (int x, list[float] coeffs)

	remove_unicode (s)
	convert unicode string to ascii

	resize_file (path, nbytes, ensure_no_overwrite=False)

	stomp_first (a, count)
	"Stomps" the first "count" elements with the first non-stomped value.

	stomp_last (a, count)
	"stomps" the last "count" elements with the last non-stomped value

	timestamp ()

	to_bool (value)

	truthy (flag)

	twos_complement (val, bits)

	uint16_to_little_endian (values)

	update_obj_from_dict (obj, d)
	given a destination object and a source dictionary, update any existing attributes of the destination object from like-named keys in the source dictionary

	vercmp (a, b, delim=".")

	wavelength_to_wavenumber (wavelength, excitation)
	convert a single wavelength to wavenumber

	wavenumber_to_wavelength (excitation, wavenumber)
	convert a single (uncorrected) wavenumber to wavelength

Variables
	log = logging.getLogger(__name__)

Function Documentation

◆ all_same()

wasatch.utils.all_same ( a )

◆ apply_boxcar()

wasatch.utils.apply_boxcar	(		a,
			half_width )

apply a boxcar convolution of the given half_width to input array 'a'

◆ area_under_peak()

wasatch.utils.area_under_peak	(	spectrum,
		x_axis,
		x_index,
		boxcar_half_width = 0 )

integrate the 'area under the curve' for the given spectrum and x_axis, using the peak centered on x_axis, with optional smoothing given the boxcar width.

◆ ascii_spectrum()

wasatch.utils.ascii_spectrum	(	spectrum,
		rows,
		cols,
		x_axis,
		x_unit )

render a spectrum as ASCII-art

◆ check_admin()

wasatch.utils.check_admin ( )

◆ clamp_to_int16()

wasatch.utils.clamp_to_int16 ( n )

◆ clean_nan()

wasatch.utils.clean_nan ( a )

◆ coeffs_look_valid()

wasatch.utils.coeffs_look_valid	(		coeffs,
			count = None )

Can be used as a sanity-check for any set of coefficients.

Checks that coeffs:

are not None
have no NaN
are not all the same (zeros, -1 etc)
are not [0, 1, 0, 0]
checks count if provided

◆ dict_get_norm()

wasatch.utils.dict_get_norm	(		d,
			keys )

Similar to dict.get(), but case-insensitive and normalizes-out spaces, underscores, periods and hyphens.

Parameters

d	(input) dictionary
k	(input) case-insensitive key (can be prioritized list)

Note that this function does not distiguish between the dictionary not having a key, and the value of the key being None.

◆ dict_get_path()

wasatch.utils.dict_get_path	(		d,
			keys )

Similar to dict.get(), but takes a list of keys to be traversed in sequence.

Parameters

d	(input) dictionary
keys	(input) list of case-insensitive keys

◆ dump()

wasatch.utils.dump	(		foo,
			indent = 0 )

similar to Perl's Data.Dumper

◆ find_local_maxima()

wasatch.utils.find_local_maxima	(	a,
		x_axis,
		center,
		tolerance = 0 )

Given a spectrum (array 'a'), with an x_axis, a 'center' along that x_axis, and an allowed 'tolerance' (in same units as the x_axis), find the local maxima within 'tolerance' of 'center'.

Note: probably a numpy shortcut for this

◆ find_nearest_index()

wasatch.utils.find_nearest_index	(		L,
			value )

◆ find_nearest_value()

wasatch.utils.find_nearest_value	(		L,
			value )

◆ find_peak_feet_indices()

wasatch.utils.find_peak_feet_indices	(	spectrum,
		x_axis,
		x_index,
		boxcar_half_width = 0 )

Given a spectrum and an x_axis, find the indexes of the left and right 'feet' of the peak centered on x_index.

Internally apply then given boxcar for added smoothing.

◆ generate_excitation()

wasatch.utils.generate_excitation	(		wavelengths,
			wavenumbers )

If we've loaded a CSV that had wavelength and wavenumber columns, but no metadata, use this to infer the excitation wavelength.

Useful for interpolation.

◆ generate_wavelengths()

wasatch.utils.generate_wavelengths	(		pixels,
			coeffs )

expand 3rd-order wavelength polynomial into array of wavelengths

◆ generate_wavelengths_from_wavenumbers()

wasatch.utils.generate_wavelengths_from_wavenumbers	(		excitation,
			wavenumbers )

◆ generate_wavenumbers()

wasatch.utils.generate_wavenumbers	(	excitation,
		wavelengths,
		wavenumber_correction = 0 )

convert wavelengths into Raman shifts in 1/cm wavenumbers from the given excitation wavelength

◆ get_pathnames_from_directory()

wasatch.utils.get_pathnames_from_directory	(	rootdir,
		pattern = None,
		recursive = False )

iterate down a directory, returning pathnames that match the given pattern

◆ interpolate_array()

wasatch.utils.interpolate_array	(	spectrum,
		old_axis,
		new_axis )

Interpolate the passed spectrum over a fixed x-axis (e.g.

integral wavelengths or wavenumbers).

◆ interpolate_value()

wasatch.utils.interpolate_value	(	spectrum,
		old_axis,
		x )

I might be making this more difficult than it needs to be.

◆ load_json()

wasatch.utils.load_json ( pathname )

convenience wrapper to load a JSON file

◆ parabolic_approximation()

wasatch.utils.parabolic_approximation	(	pixel,
		x,
		y )

Given an array of doubles and a peak index, use the peak and its two neighbors to form a parabola and return the interpolated maximum height of the parabola.

"pixel" is ideally the array index of the pinnacle of a previously- identified peak within the spectrum, although though this will technically generate a parabola through any pixel and its two neighbors.

Parameters

pixel	index of a point on the spectrum
x	x-axis (wavelengths or wavenumbers)
y	y-axis (intensity)

See also: https://stackoverflow.com/a/717833

Returns: a point representing the interpolated vertex of a parabola drawn through the specified pixel and its two neighbors (in x-axis space)

◆ peak_height_above_background()

wasatch.utils.peak_height_above_background	(	spectrum,
		x_axis,
		x_index,
		boxcar_half_width = 0 )

◆ pixel_to_wavelength()

wasatch.utils.pixel_to_wavelength	(	int	x,
		list[float]	coeffs )

◆ remove_unicode()

wasatch.utils.remove_unicode ( s )

convert unicode string to ascii

◆ resize_file()

wasatch.utils.resize_file	(	path,
		nbytes,
		ensure_no_overwrite = False )

Keep only the last `nbytes` of a text file specified by `path`.

This function is encoding (utf-8, utf-16, ascii) agnostic because it uses the line-
continuation character '\n' as a boundary. It is system independent because it slices
before that character. This was written to keep our default log file within a reasonable
filesize, suitable for emailing.

Call this on a file that is not yet opened. Expect the file to be closed and flushed
by the time the function returns.

returns True if the file was changed.

◆ stomp_first()

wasatch.utils.stomp_first	(		a,
			count )

"Stomps" the first "count" elements with the first non-stomped value.

Parameters

a	(Input) array to modify
count	(Input) HOW MANY leading elements to stomp, so the index of the first GOOD pixel should be one more than this

◆ stomp_last()

wasatch.utils.stomp_last	(		a,
			count )

"stomps" the last "count" elements with the last non-stomped value

◆ timestamp()

wasatch.utils.timestamp ( )

◆ to_bool()

wasatch.utils.to_bool ( value )

◆ truthy()

wasatch.utils.truthy ( flag )

◆ twos_complement()

wasatch.utils.twos_complement	(		val,
			bits )

See also: https://stackoverflow.com/a/9147327/6436775

◆ uint16_to_little_endian()

wasatch.utils.uint16_to_little_endian ( values )

◆ update_obj_from_dict()

wasatch.utils.update_obj_from_dict	(		obj,
			d )

given a destination object and a source dictionary, update any existing attributes of the destination object from like-named keys in the source dictionary

Parameters

obj	(In/Out) the object whose attributes to update
d	(Input) the dictionary whose keys should be treated as attributes

◆ vercmp()

wasatch.utils.vercmp	(	a,
		b,
		delim = "." )

 vercmp("1.2.3.4", "1.2.4.3") -> -1

◆ wavelength_to_wavenumber()

wasatch.utils.wavelength_to_wavenumber	(		wavelength,
			excitation )

convert a single wavelength to wavenumber

◆ wavenumber_to_wavelength()

wasatch.utils.wavenumber_to_wavelength	(		excitation,
			wavenumber )

convert a single (uncorrected) wavenumber to wavelength

Variable Documentation

◆ log

wasatch.utils.log = logging.getLogger(__name__)

Functions

Variables

Function Documentation

◆ all_same()

◆ apply_boxcar()

◆ area_under_peak()

◆ ascii_spectrum()

◆ check_admin()

◆ clamp_to_int16()

◆ clean_nan()

◆ coeffs_look_valid()

◆ dict_get_norm()

◆ dict_get_path()

◆ dump()

◆ find_local_maxima()

◆ find_nearest_index()

◆ find_nearest_value()

◆ find_peak_feet_indices()

◆ generate_excitation()

◆ generate_wavelengths()

◆ generate_wavelengths_from_wavenumbers()

◆ generate_wavenumbers()

◆ get_pathnames_from_directory()

◆ interpolate_array()

◆ interpolate_value()

◆ load_json()

◆ parabolic_approximation()

◆ peak_height_above_background()

◆ pixel_to_wavelength()

◆ remove_unicode()

◆ resize_file()

◆ stomp_first()

◆ stomp_last()

◆ timestamp()

◆ to_bool()

◆ truthy()

◆ twos_complement()

◆ uint16_to_little_endian()

◆ update_obj_from_dict()

◆ vercmp()

◆ wavelength_to_wavenumber()

◆ wavenumber_to_wavelength()

Variable Documentation

◆ log